First of all, we set a seed for reproducibility of the results.
# Set the seed for random number generation to ensure reproducibility of results
set.seed(42)
We are astronomy researchers in a private company that works by
founding and we are in charge of categorizing observed space objects as
either stars, galaxies or quasar objects (QSO). We have a dataset that
provides us with 100000 observations of space taken by the SDSS (Sloan
Digital Sky Survey). Our classification task is very important because
we are later going to analyse the objects and depending on what they
are, we are going to receive more or less money for its study (more for
quasar objects). Hence, we are going to focus on the variable
calls that tells us the object class out of the three
mentioned possible ones.
We are in charge of creating a report with the number of each of this objects to submit at the end of the month in order to receive the corresponding funding the following month, that is why we are interested in predicting the class of these objects to be able to ask for the amount of money we need and not end up with more or less of what we need, because if we end with less we can’t carry out every test we need to study the object, and if we receive more we will have a penalty on next month funding.
As we said, the data consists of 100000 observations of space objects taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar (our target variable).
In summary, we are going to use the variables in our dataset to
predict the variable class, which we are going to transform
into a binary variable with levels ‘QSO’ and ‘NOT.QSO’. We are going to
use the machine learning methods seen in class (k-nearest neighbours,
support vector machines, decision trees, random forest, gradient
boosting neural networks and deep neural networks).
First of all, we load our dataset.
data_whole <- read.csv("star_classification.csv")
head(data_whole)
## obj_ID alpha delta u g r i z
## 1 1.237679e+18 341.4055 8.578359 19.92760 18.16381 17.06835 16.56405 16.16184
## 2 1.237679e+18 357.7307 11.390703 21.31802 21.14918 21.06537 20.85919 20.71210
## 3 1.237659e+18 246.1227 35.108206 20.76552 20.74977 20.81548 20.50528 20.86766
## 4 1.237661e+18 197.5328 48.984102 23.29459 21.82885 20.12599 19.10921 18.81542
## 5 1.237662e+18 195.3274 42.810901 22.92567 21.65787 20.03059 19.57382 19.39124
## 6 1.237658e+18 126.9168 30.977271 19.47264 17.61016 16.82478 16.52191 16.32555
## run_ID rerun_ID cam_col field_ID spec_obj_ID class redshift plate MJD
## 1 7808 301 2 84 5.696147e+18 GALAXY 0.1418661000 5059 56190
## 2 7773 301 1 295 1.299978e+19 QSO 1.9429430000 11546 58488
## 3 3185 301 4 39 1.207754e+19 QSO 1.8896200000 10727 58197
## 4 3650 301 3 64 7.603369e+18 GALAXY 0.5099247000 6753 56399
## 5 3893 301 5 226 1.641713e+18 GALAXY 0.4178358000 1458 53119
## 6 2963 301 1 29 3.582649e+18 STAR 0.0001985597 3182 54828
## fiber_ID
## 1 798
## 2 506
## 3 38
## 4 607
## 5 549
## 6 128
We can see that we have the following 18 variables:
obj_ID: Object Identifier, the unique value that
identifies the object in the image catalog used by the CAS.alpha: Right Ascension angle (at J2000 epoch).delta: Declination angle (at J2000 epoch).u: Ultraviolet filter in the photometric system.g: Green filter in the photometric system.r: Red filter in the photometric system.i: Near Infrared filter in the photometric system.z: Infrared filter in the photometric system.run_ID: Run Number used to identify the specific
scan.rereun_ID: Rerun Number to specify how the image was
processed.cam_col: Camera column to identify the scanline within
the run.field_ID: Field number to identify each field.spec_obj_ID: Unique ID used for optical spectroscopic
objects (this means that 2 different observations with the same
spec_obj_ID must share the output class).class: Object class (galaxy, star or quasar
object).redshift: Redshift value based on the increase in
wavelength.plate: Plate ID, identifies each plate in SDSS.MJD: Modified Julian Date, used to indicate when a
given piece of SDSS data was taken.fiber_ID: Fiber ID that identifies the fiber that
pointed the light at the focal plane in each observation.We have 14 numerical variables (obj_ID,
alpha, delta, u, g,
r, i, z, run_ID,
rerun_ID, redshift, field_ID,
spec_obj_ID and fiber_ID), 3 integer variables
(cam_col, plate and MJD) and one
multicategorical, our target variable class.
First of all, we are going to eliminate the variable
obj_ID because it is an individual identification number
that does not provide any information. We are also going to eliminate
the rerun_ID variable because it is constant with value 301
in all the observations.
In addition, we are going to make the run_ID variable
multicategorical by creating categories for the numbers every 1000. In
other words, the observations with values in this variable between 0 and
1000 are all part of the ‘0-1000’ category, the ones with value between
1001 and 2000 are in the category 1000-2000, and so on. Afterwards, we
are going to set all the categorical variables (class,
run_ID and cam_col) as factors.
# Eliminate the specified variables from the dataset
data = data_whole[,-c(1, 10)]
# Make multicategorical variables
# Define the breaks for categorization
breaks <- seq(0, 9000, by = 1000)
# Create a new categorical variable based on 'run_ID'
data$run_ID <- cut(data$run_ID, breaks = breaks, labels = paste(breaks[-length(breaks)], "-", breaks[-1]))
# Convert categorical variables to factor type
data[, c('class', 'run_ID', 'cam_col')] <- lapply(data[, c('class', 'run_ID', 'cam_col')], function(x) as.factor(x))
# Display the structure and first few rows of the preprocessed dataset
head(data)
## alpha delta u g r i z run_ID
## 1 341.4055 8.578359 19.92760 18.16381 17.06835 16.56405 16.16184 7000 - 8000
## 2 357.7307 11.390703 21.31802 21.14918 21.06537 20.85919 20.71210 7000 - 8000
## 3 246.1227 35.108206 20.76552 20.74977 20.81548 20.50528 20.86766 3000 - 4000
## 4 197.5328 48.984102 23.29459 21.82885 20.12599 19.10921 18.81542 3000 - 4000
## 5 195.3274 42.810901 22.92567 21.65787 20.03059 19.57382 19.39124 3000 - 4000
## 6 126.9168 30.977271 19.47264 17.61016 16.82478 16.52191 16.32555 2000 - 3000
## cam_col field_ID spec_obj_ID class redshift plate MJD fiber_ID
## 1 2 84 5.696147e+18 GALAXY 0.1418661000 5059 56190 798
## 2 1 295 1.299978e+19 QSO 1.9429430000 11546 58488 506
## 3 4 39 1.207754e+19 QSO 1.8896200000 10727 58197 38
## 4 3 64 7.603369e+18 GALAXY 0.5099247000 6753 56399 607
## 5 5 226 1.641713e+18 GALAXY 0.4178358000 1458 53119 549
## 6 1 29 3.582649e+18 STAR 0.0001985597 3182 54828 128
As we mentioned, we are going to make our target variable binary by combining the ‘Star’ and ‘Galaxy’ categories into a level called ‘NOT.QSO’.
# Recode the levels of the class variable
data$class <- factor(data$class, levels = c("STAR", "GALAXY", "QSO"))
# Create a new binary variable with custom labels
data$class <- factor(ifelse(data$class %in% c("STAR", "GALAXY"), "NOT.QSO", "QSO"), levels = c("NOT.QSO", "QSO"))
Now we wanna study the variables and to do that we compute a summary to see some details like the mean, median, minimum, maximum, missing values (NA’s),…
# Summarize the data frame
summary(data)
## alpha delta u g
## Min. : 0.0298 Min. :-18.785 Min. :-9999.00 Min. :-9999.00
## 1st Qu.:126.4262 1st Qu.: 4.612 1st Qu.: 20.37 1st Qu.: 18.99
## Median :177.6281 Median : 23.740 Median : 22.18 Median : 21.14
## Mean :174.7733 Mean : 24.126 Mean : 21.09 Mean : 19.57
## 3rd Qu.:231.6237 3rd Qu.: 40.398 3rd Qu.: 23.69 3rd Qu.: 22.13
## Max. :359.9970 Max. : 82.757 Max. : 29.23 Max. : 29.86
## NA's :935 NA's :747
## r i z run_ID
## Min. : 9.822 Min. : 9.47 Min. :-9999.00 3000 - 4000:2383
## 1st Qu.:18.201 1st Qu.:17.80 1st Qu.: 17.52 4000 - 5000:1900
## Median :20.160 Median :19.41 Median : 19.00 2000 - 3000:1522
## Mean :19.660 Mean :19.10 Mean : 17.79 5000 - 6000:1289
## 3rd Qu.:21.037 3rd Qu.:20.44 3rd Qu.: 19.94 7000 - 8000:1207
## Max. :29.572 Max. :32.14 Max. : 28.79 1000 - 2000: 795
## NA's :932 (Other) : 904
## cam_col field_ID spec_obj_ID class
## 1:1357 Min. : 11.0 Min. :3.006e+17 NOT.QSO:8083
## 2:1730 1st Qu.: 83.0 1st Qu.:2.880e+18 QSO :1917
## 3:1911 Median :148.0 Median :5.631e+18
## 4:1930 Mean :186.7 Mean :5.824e+18
## 5:1823 3rd Qu.:240.0 3rd Qu.:8.344e+18
## 6:1249 Max. :980.0 Max. :1.413e+19
##
## redshift plate MJD fiber_ID
## Min. :-0.004136 Min. : 267 Min. :51608 Min. : 1.0
## 1st Qu.: 0.050320 1st Qu.: 2558 1st Qu.:54270 1st Qu.: 216.0
## Median : 0.426655 Median : 5001 Median :55896 Median : 431.0
## Mean : 0.578574 Mean : 5173 Mean :55607 Mean : 446.9
## 3rd Qu.: 0.707591 3rd Qu.: 7411 3rd Qu.:56799 3rd Qu.: 641.0
## Max. : 7.010272 Max. :12547 Max. :58932 Max. :1000.0
## NA's :761
We see that in the variables u, g and
z something is up because the minimum is -9999, which can
not be. If we look at the dataset we can see that the observations with
those values in those variables are the same one, so we are going to
eliminate said observation.
which(data$u == -9999)
## [1] 2045
which(data$g == -9999)
## [1] 2045
which(data$z == -9999)
## [1] 2045
As we said, we can see that it is the same observation (observation 2045), so we eliminate it from our dataset.
data = data[-2045, ]
Now, we compute the summary again and we see that nothing of the sorts happens in any other variable and that it still does not happen in the ones mentioned.
summary(data)
## alpha delta u g
## Min. : 0.0298 Min. :-18.785 Min. :14.15 Min. :10.73
## 1st Qu.:126.4219 1st Qu.: 4.615 1st Qu.:20.37 1st Qu.:18.99
## Median :177.6225 Median : 23.746 Median :22.18 Median :21.14
## Mean :174.7684 Mean : 24.129 Mean :22.09 Mean :20.65
## 3rd Qu.:231.6335 3rd Qu.: 40.400 3rd Qu.:23.69 3rd Qu.:22.13
## Max. :359.9970 Max. : 82.757 Max. :29.23 Max. :29.86
## NA's :935 NA's :747
## r i z run_ID cam_col
## Min. : 9.822 Min. : 9.47 Min. : 9.612 3000 - 4000:2383 1:1357
## 1st Qu.:18.201 1st Qu.:17.80 1st Qu.:17.521 4000 - 5000:1900 2:1729
## Median :20.160 Median :19.41 Median :19.005 2000 - 3000:1522 3:1911
## Mean :19.660 Mean :19.10 Mean :18.790 5000 - 6000:1289 4:1930
## 3rd Qu.:21.037 3rd Qu.:20.44 3rd Qu.:19.942 7000 - 8000:1207 5:1823
## Max. :29.572 Max. :32.14 Max. :28.791 1000 - 2000: 795 6:1249
## NA's :932 (Other) : 903
## field_ID spec_obj_ID class redshift
## Min. : 11.0 Min. :3.006e+17 NOT.QSO:8082 Min. :-0.004136
## 1st Qu.: 83.0 1st Qu.:2.880e+18 QSO :1917 1st Qu.: 0.050434
## Median :148.0 Median :5.631e+18 Median : 0.426959
## Mean :186.6 Mean :5.824e+18 Mean : 0.578632
## 3rd Qu.:240.0 3rd Qu.:8.344e+18 3rd Qu.: 0.707636
## Max. :980.0 Max. :1.413e+19 Max. : 7.010272
##
## plate MJD fiber_ID
## Min. : 267 Min. :51608 Min. : 1.0
## 1st Qu.: 2558 1st Qu.:54270 1st Qu.: 216.5
## Median : 5001 Median :55896 Median : 431.0
## Mean : 5173 Mean :55607 Mean : 447.0
## 3rd Qu.: 7411 3rd Qu.:56799 3rd Qu.: 641.0
## Max. :12547 Max. :58932 Max. :1000.0
## NA's :761
We are interested in dealing with the missing values and getting rid of them in the best way we can. We compute the percentage of observations with missing values in each variable.
# Compute the percentage of missing values for each variable
sapply(data, function(x) mean(is.na(x)) * 100)
## alpha delta u g r i
## 0.000000 9.350935 0.000000 7.470747 9.320932 0.000000
## z run_ID cam_col field_ID spec_obj_ID class
## 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
## redshift plate MJD fiber_ID
## 0.000000 0.000000 7.610761 0.000000
We can see that we have missing values in the variables
delta, g, r and MJD.
However, we do not eliminate these variables, instead what we do is
compute the missing values. We decide not to eliminate them because the
percentage of missing values is very low in each variable:
delta has 9.35%, g has 7.47%, r
has 9.32% and MJD has 7.61%.
The missing values are all in numeric variables, so we want to see the distributions of the data and if they have outliers, so we compute the histograms (to see skewness and symmetry) and boxplots (to see outliers) of each variable. If the variable is normally distributed or approximately symmetric without extreme outliers we exchange the NA’s with the mean of the variable. However, if it is heavily skewed or with outliers, we substitute it with the median.
# Take only the variables with missing values
missing_vars = c("delta", "g", "r", "MJD")
# Create histograms for symmetry and skewness of each variable
for(var in missing_vars) {
# Plot histogram for the current variable
hist(data[[var]],
main = paste("Histogram of", var), # Title of the plot
xlab = var, # Label for x-axis
ylab = "Frequency", # Label for y-axis
col = "skyblue", # Fill color of bars
border = "black" # Border color of bars
)
}
We also compute the skewness.
selected_data <- data_whole[, missing_vars]
apply(selected_data, 2, skewness, na.rm = TRUE)
## delta g r MJD
## 0.1665573 -96.1064511 -0.5321120 -0.3903255
Such strong skewness on the variable g suggest the
presence of many extreme outliers. And now the boxplots to check for
outliers. We compute the ones corresponding to the variables
delta, ag and r separate from the
one corresponding to MJD because of the scaling and, hence,
to visualize it better.
# Extract the columns with missing values from the dataset
missing = setdiff(missing_vars, 'MJD')
missing = data[,missing]
# Create boxplots to identify outliers in each variable
boxplot(missing, # Data for boxplot
outline = TRUE, # Show outliers
col = 'skyblue', # Fill color of boxes
border = 'black' # Border color of boxes
)
# Create boxplot to identify outliers in MJD
boxplot(data[, 'MJD' ], # Data for boxplot
outline = TRUE, # Show outliers
col = 'skyblue', # Fill color of boxes
border = 'black', # Border color of boxes
xlab = 'MJD'
)
We can see, following the criteria we said before, that we have to
use the mean in the variable delta, and the median in the
variables g, r and MJD.
# Replace missing values with mean for delta
data$delta = replace_na(data$delta, median(data$delta, na.rm=TRUE))
# Replace missing values with median for g
data$g = replace_na(data$g, median(data$g, na.rm=TRUE))
# Replace missing values with median for r
data$r = replace_na(data$r, median(data$r, na.rm=TRUE))
# Replace missing values with median for MJD
data$MJD = replace_na(data$MJD, median(data$MJD, na.rm=TRUE))
We check that there are no remaining missing values with the following function.
# Function to check for missing values
check_missing_values <- function(data) {
if (any(is.na(data))) { # Check if any missing values exist
message("There are missing values left.") # Print a message if missing values are found
} else {
message("There are no missing values left.") # Print a message if no missing values are found
}
}
# Call the function with our dataset
check_missing_values(data) # Check missing values in data dataset
## There are no missing values left.
We can see in the following code that the classes are not well balanced, they are, in fact, extremely unbalanced (80.82% NOT.QSO and 19.18% QSO).
# Display the frequency table of the 'temp' variable
table(data$class)
##
## NOT.QSO QSO
## 8082 1917
After creating classifiers and training them, we will want to see how well they perform, how accurate they are. To do this, we need to do a train/test partition. We do this so we can use the train partition to fit the model and the test partition to measure its performance. We make a partition with 40% on the training and 60% on the testing because of the high volume of observations we have.
The train/test partition helps to prevent overfitting, which occurs when a model learns the training data too well and performs poorly on new data. By evaluating the model on a separate testing set, you can get a more accurate estimate of its performance on unseen data.
# Shuffle the rows of the dataset
shuffled_data <- data[sample(nrow(data)), ]
# Create a train/test partition with the shuffled data
train_index <- createDataPartition(
y = shuffled_data$class, # Specify the response variable for stratified sampling
p = 0.4, # Proportion of data for the training set, 40%
list = FALSE # Return indices as a vector, not as a list
)
# Create the training and testing datasets
train_data <- shuffled_data[train_index, ] # Subset of shuffled_data for training
test_data <- shuffled_data[-train_index, ] # Subset of shuffled_data for testing
Exploratory Data Analysis (EDA) is an approach to analyzing datasets in order to summarize their main characteristics, often using visual methods. Now that we have done the partition, we are going to carry this kind of analysis on the train dataset.
Here we observe the distributions of the numerical variables via a
histogram of frequency for each variable to see their distributions and
have an idea about them. We separate it by their values on the variable
class.
# Define numeric variables
numeric_vars <- c('alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'field_ID', 'spec_obj_ID', 'redshift', 'plate', 'MJD', 'fiber_ID')
# Create histograms for each numeric variable
for (var in numeric_vars) {
# Create a ggplot object
hist_plot <- ggplot(train_data, aes(x = !!sym(var), fill = class)) +
geom_histogram(bins = 30, alpha = 0.5, position = "identity") +
labs(title = paste("Histogram of", var),
x = var,
y = "Frequency") +
scale_fill_manual(values = c("skyblue", "salmon")) # Define colors for classes
# Print the histogram
print(hist_plot)
}
We can see that in the variable u, non quasar objects
take more extreme values than quasar objects. If an object has an
observation in this variable that is less than 17 or more than 27, then
it is a non quasar object. Moreover, in the variable delta
we can see that both classes take the same range of values, they differ
in frequency but because of the unbalance in the number of observations.
In the variable i, we can also see that those observations
with values lower than 17 are non quasar objects and with more than 17
can be both.
Now, we are going to study the other variables, the categorical ones. We see how the observations in our categorical variables are distributed via barplots for each variable.
# Define categorical variables
categorical_vars <- c('run_ID', 'cam_col', 'class')
# Create bar plots for each categorical variable in the training dataset
for(var in categorical_vars) {
# Compute frequency table for the current categorical variable
freq_table <- table(train_data[[var]])
# Create a bar plot
barplot(freq_table, # Data for bar plot
main = paste("Bar plot of", var), # Title of the bar plot
xlab = var, # Label for x-axis
ylab = "Frequency", # Label for y-axis
col = "skyblue", # Fill color of bars
border = "black" # Border color of bars
)
}
In the barplot of our target variable class, we can once
again see the unbalance of our two categories. If we observe a new
object and make a prediction based on this barplot, we would classify
all of them as ‘NOT.QSO’. This is what a naive classifier does, classify
on the more numerous class.
We are going to explore some of the combinations of variables by
pairs of numeric variables to see the relationship between them, but
also their relationship with the target variable. I think it is
important to see these plots, so we can see that we can observe other
things not involving our target variable. For example,
delta and `redshift``.
# Scatter plot using ggplot2
ggplot(train_data, aes(x = delta, y = redshift, color = class)) + # Specify the data and aesthetics
geom_point(alpha = 0.5) + # Add points with transparency and color
labs(title = "Scatter plot of delta vs. redshift", # Set the title and axis labels
x = "Delta",
y = "Redshift") +
theme(plot.title = element_text(hjust = 0.5)) # Adjust the title position
we can see that, it does not follow any pattern. It does not look
like there is any correlation between the two variables. All values of
delta take all values of redshift. However,
what we can observe is that the non quasar objects take lower values in
the redshift variable, the higher values are only taken by
the quasar objects.
If we think about it, two variables that seem reasonable that will be
correlated are r and g, because both are
related to colour filters in the photometric system. We expect to
observe a pattern in the plot.
# Scatter plot using ggplot2
ggplot(train_data, aes(x = r, y = g, color = class)) + # Specify the data and aesthetics
geom_point(alpha = 0.5) + # Add points with transparency and color
labs(title = "Scatter plot of r vs. g", # Set the title and axis labels
x = "r",
y = "g") +
theme(plot.title = element_text(hjust = 0.5)) # Adjust the title position
We, in fact, do observe a pattern that indicates high correlation. However, we have some observations that do not follow the pattern, which show that while we have high correlation, we do not have extremely high correlation. We will see it in the correlation matrix we will later compute with the corresponding value being high, but not extremely close to 1. Here, we can not draw any conclusion related to our target variable because the distribution is similar.
Another pair of variables that seems reasonable to think that will
have this high correlation are u and g because
of the same reason, both are related to the colour filter in the
photometric system.
# Scatter plot using ggplot2
ggplot(train_data, aes(x = r, y = i, color = class)) + # Specify the data and aesthetics
geom_point(alpha = 0.5) + # Add points with transparency and color
labs(title = "Scatter plot of r vs. i", # Set the title and axis labels
x = "r",
y = "i") +
theme(plot.title = element_text(hjust = 0.5)) # Adjust the title position
We observe the pattern we expected, one that supports our idea of the variables being highly correlated, but not perfectly. Once again, we can not draw any conclusion on our target variable from this plot.
Another pair of variables that seems interesting to plot are
alpha and deltaand we will in fact observe
some clusters, but we will not be able to draw any conclusion on our
target variable.
# Scatter plot using ggplot2
ggplot(train_data, aes(x = alpha, y = delta, color = class)) + # Specify the data and aesthetics
geom_point(alpha = 0.5) + # Add points with transparency and color
labs(title = "Scatter plot of alpha vs. delta", # Set the title and axis labels
x = "Alpha",
y = "Delta") +
theme(plot.title = element_text(hjust = 0.5)) # Adjust the title position
We are now going to use boxplots to observe other pairs of variables
where at least one will be our target variable (we use boxplots because
class is categorical and they are more informative with
these variables). For example, it may be interesting to see the relation
between class and redshift.
# Boxplot using ggplot2
ggplot(train_data, aes(x = class, y = redshift)) + # Specify the data and aesthetics
geom_boxplot(fill = "skyblue", color = "black") + # Add boxplots with fill and border color
labs(title = "Box plot of redshift by class", # Set the title and axis labels
x = "Class",
y = "Redshift") +
theme(plot.title = element_text(face = "bold", hjust = 0.5)) # Adjust the title font and position
Here, we can clearly see that the bigger values (more than 1) of
redshift correspond to observations of quasar objects (as we saw in the
first scatterplot). Let’s see if we can observe something similar with
other variables and our target variable. For example, class
with i.
# Boxplot using ggplot2
ggplot(train_data, aes(x = class, y = i)) + # Specify the data and aesthetics
geom_boxplot(fill = "skyblue", color = "black") + # Add boxplots with fill and border color
labs(title = "Box plot of i by class", # Set the title and axis labels
x = "Class",
y = "i") +
theme(plot.title = element_text(face = "bold", hjust = 0.5)) # Adjust the title font and position
Here, we can see that values of i bigger than 20
correspond to QSO and lower values correspond to NOT.QSO. This is
something that we were not able to see in the scatterplot.
As a final bivariate analysis, we are going to compute the correlation matrix, but only of the numeric variables. Here, we will be able to check if our conclusion drawn from plots are true or not about the correlation of the observed matrices.
# Compute the correlation matrix for numeric variables in the training dataset
correlation_matrix <- cor(train_data[, numeric_vars])
correlation_matrix
## alpha delta u g r
## alpha 1.0000000000 0.1362838132 0.02559222 -0.005500065 -0.006899563
## delta 0.1362838132 1.0000000000 -0.02216318 -0.002474210 -0.006307691
## u 0.0255922244 -0.0221631829 1.00000000 0.822362714 0.690649355
## g -0.0055000647 -0.0024742100 0.82236271 1.000000000 0.854288479
## r -0.0068995630 -0.0063076906 0.69064936 0.854288479 1.000000000
## i -0.0175212355 -0.0003301599 0.60871598 0.811336104 0.914781634
## z -0.0185645409 -0.0028896333 0.54142889 0.751061711 0.878134319
## field_ID -0.1285622311 -0.1468639545 -0.03863537 -0.047378504 -0.043615142
## spec_obj_ID -0.0239720944 0.0804291749 0.37730968 0.552109899 0.611419472
## redshift -0.0002882211 -0.0056673573 0.16034643 0.296420783 0.408272583
## plate -0.0239735297 0.0804292969 0.37730805 0.552108688 0.611418224
## MJD -0.0032040014 0.0765470790 0.40026700 0.552483401 0.601216456
## fiber_ID 0.0584600427 0.0143624941 0.16648555 0.190467585 0.206764500
## i z field_ID spec_obj_ID redshift
## alpha -0.0175212355 -0.018564541 -0.12856223 -0.02397209 -0.0002882211
## delta -0.0003301599 -0.002889633 -0.14686395 0.08042917 -0.0056673573
## u 0.6087159751 0.541428889 -0.03863537 0.37730968 0.1603464271
## g 0.8113361036 0.751061711 -0.04737850 0.55210990 0.2964207826
## r 0.9147816336 0.878134319 -0.04361514 0.61141947 0.4082725833
## i 1.0000000000 0.973451958 -0.03770986 0.65606604 0.4832415491
## z 0.9734519584 1.000000000 -0.03814630 0.64821589 0.4912012449
## field_ID -0.0377098631 -0.038146304 1.00000000 -0.06314366 -0.0161698096
## spec_obj_ID 0.6560660386 0.648215891 -0.06314366 1.00000000 0.3869796667
## redshift 0.4832415491 0.491201245 -0.01616981 0.38697967 1.0000000000
## plate 0.6560648677 0.648214883 -0.06314376 1.00000000 0.3869785409
## MJD 0.6381283714 0.628586074 -0.06246765 0.92723968 0.3702241153
## fiber_ID 0.2143259621 0.205066962 -0.01096934 0.24666142 0.1459373850
## plate MJD fiber_ID
## alpha -0.02397353 -0.003204001 0.05846004
## delta 0.08042930 0.076547079 0.01436249
## u 0.37730805 0.400267003 0.16648555
## g 0.55210869 0.552483401 0.19046758
## r 0.61141822 0.601216456 0.20676450
## i 0.65606487 0.638128371 0.21432596
## z 0.64821488 0.628586074 0.20506696
## field_ID -0.06314376 -0.062467646 -0.01096934
## spec_obj_ID 1.00000000 0.927239675 0.24666142
## redshift 0.38697854 0.370224115 0.14593738
## plate 1.00000000 0.927239149 0.24664049
## MJD 0.92723915 1.000000000 0.25226180
## fiber_ID 0.24664049 0.252261798 1.00000000
Here, we can corroborate what we showed before: r and
g are highly correlated (0.84), as well as r
and i (0.92); while delta and
redshift are not (0.006), as well as alpha and
beta (0.17).
Now, we are going to compute a heat map of the previously computed correlation matrix.
# Convert correlation matrix to a matrix
cor_matrix <- as.matrix(correlation_matrix)
# Create the correlation heatmap
heatmap(cor_matrix,
Rowv = NA, # Do not reorder rows
Colv = NA, # Do not reorder columns
col = colorRampPalette(c("blue", "white", "red"))(100), # Define color scheme
scale = "none", # Do not scale rows or columns
main = "Correlation heatmap of numeric variables" # Set the title of the plot
)
# Add color legend
legend("bottomright",
legend = c("Low", "Medium", "High"), # Legend labels
fill = colorRampPalette(c("blue", "white", "red"))(3), # Colors corresponding to legend labels
title = "Correlation", # Title of the legend
bg = "transparent" # Transparent background
)
We can observe once again that r and g are
highly correlated, as well as r and i, but
delta and redshift are not and neither
alpha and beta. On top of that, we can see how
correlated are each and every pair of variables. As the legend says, the
redder the square, the more correlated they are and the bluest, the
lowest correlation.
A benchmark model serves as a baseline for comparison when developing or evaluating more complex models. It’s typically a simple, easy-to-understand model that provides a reference point for assessing the performance of more sophisticated algorithms.
The purpose of a benchmark model is to establish a minimum level of performance that any new model must surpass to be considered useful. It helps in gauging whether the additional complexity of a more advanced model is justified by a significant improvement in performance. Additionally, benchmark models are useful for understanding the difficulty of the task and for providing a point of comparison across different approaches or datasets.
Since we have many predictors, we saw in class that a good benchmark will be penalized logistic regression. Penalized logistic regression is a modification of logistic regression that incorporates regularization techniques to prevent overfitting and improve generalization performance. It introduces penalty terms to the loss function, discouraging large coefficient values. It addresses issues such as multicollinearity, overfitting, and high dimensionality.
Firstly, we need the control function which implements cross-validation with 5 folds.
ctrllog <- trainControl(method = "cv", number = 5,
classProbs = TRUE,
verboseIter=T)
Now, we can fit our model using the caret package and incorporating our control function. Moreover, we take Kappa as our accuracy metric.
# Train the logistic regression model using penalized logistic regression (glmnet)
lrFit <- train(class ~ ., # Predict class based on all predictors
method = "glmnet", # Use glmnet method for logistic regression
tuneGrid = expand.grid(alpha = seq(0, 1, 0.1), lambda = seq(0, .1, 0.02)), # Hyperparameter grid for alpha and lambda
metric = "Kappa", # Use Kappa as the evaluation metric
data = train_data, # Training data
preProcess = c("center", "scale"), # Preprocess data by centering and scaling
trControl = ctrllog) # Use defined control object for cross-validation
## + Fold1: alpha=0.0, lambda=0.1
## - Fold1: alpha=0.0, lambda=0.1
## + Fold1: alpha=0.1, lambda=0.1
## - Fold1: alpha=0.1, lambda=0.1
## + Fold1: alpha=0.2, lambda=0.1
## - Fold1: alpha=0.2, lambda=0.1
## + Fold1: alpha=0.3, lambda=0.1
## - Fold1: alpha=0.3, lambda=0.1
## + Fold1: alpha=0.4, lambda=0.1
## - Fold1: alpha=0.4, lambda=0.1
## + Fold1: alpha=0.5, lambda=0.1
## - Fold1: alpha=0.5, lambda=0.1
## + Fold1: alpha=0.6, lambda=0.1
## - Fold1: alpha=0.6, lambda=0.1
## + Fold1: alpha=0.7, lambda=0.1
## - Fold1: alpha=0.7, lambda=0.1
## + Fold1: alpha=0.8, lambda=0.1
## - Fold1: alpha=0.8, lambda=0.1
## + Fold1: alpha=0.9, lambda=0.1
## - Fold1: alpha=0.9, lambda=0.1
## + Fold1: alpha=1.0, lambda=0.1
## - Fold1: alpha=1.0, lambda=0.1
## + Fold2: alpha=0.0, lambda=0.1
## - Fold2: alpha=0.0, lambda=0.1
## + Fold2: alpha=0.1, lambda=0.1
## - Fold2: alpha=0.1, lambda=0.1
## + Fold2: alpha=0.2, lambda=0.1
## - Fold2: alpha=0.2, lambda=0.1
## + Fold2: alpha=0.3, lambda=0.1
## - Fold2: alpha=0.3, lambda=0.1
## + Fold2: alpha=0.4, lambda=0.1
## - Fold2: alpha=0.4, lambda=0.1
## + Fold2: alpha=0.5, lambda=0.1
## - Fold2: alpha=0.5, lambda=0.1
## + Fold2: alpha=0.6, lambda=0.1
## - Fold2: alpha=0.6, lambda=0.1
## + Fold2: alpha=0.7, lambda=0.1
## - Fold2: alpha=0.7, lambda=0.1
## + Fold2: alpha=0.8, lambda=0.1
## - Fold2: alpha=0.8, lambda=0.1
## + Fold2: alpha=0.9, lambda=0.1
## - Fold2: alpha=0.9, lambda=0.1
## + Fold2: alpha=1.0, lambda=0.1
## - Fold2: alpha=1.0, lambda=0.1
## + Fold3: alpha=0.0, lambda=0.1
## - Fold3: alpha=0.0, lambda=0.1
## + Fold3: alpha=0.1, lambda=0.1
## - Fold3: alpha=0.1, lambda=0.1
## + Fold3: alpha=0.2, lambda=0.1
## - Fold3: alpha=0.2, lambda=0.1
## + Fold3: alpha=0.3, lambda=0.1
## - Fold3: alpha=0.3, lambda=0.1
## + Fold3: alpha=0.4, lambda=0.1
## - Fold3: alpha=0.4, lambda=0.1
## + Fold3: alpha=0.5, lambda=0.1
## - Fold3: alpha=0.5, lambda=0.1
## + Fold3: alpha=0.6, lambda=0.1
## - Fold3: alpha=0.6, lambda=0.1
## + Fold3: alpha=0.7, lambda=0.1
## - Fold3: alpha=0.7, lambda=0.1
## + Fold3: alpha=0.8, lambda=0.1
## - Fold3: alpha=0.8, lambda=0.1
## + Fold3: alpha=0.9, lambda=0.1
## - Fold3: alpha=0.9, lambda=0.1
## + Fold3: alpha=1.0, lambda=0.1
## - Fold3: alpha=1.0, lambda=0.1
## + Fold4: alpha=0.0, lambda=0.1
## - Fold4: alpha=0.0, lambda=0.1
## + Fold4: alpha=0.1, lambda=0.1
## - Fold4: alpha=0.1, lambda=0.1
## + Fold4: alpha=0.2, lambda=0.1
## - Fold4: alpha=0.2, lambda=0.1
## + Fold4: alpha=0.3, lambda=0.1
## - Fold4: alpha=0.3, lambda=0.1
## + Fold4: alpha=0.4, lambda=0.1
## - Fold4: alpha=0.4, lambda=0.1
## + Fold4: alpha=0.5, lambda=0.1
## - Fold4: alpha=0.5, lambda=0.1
## + Fold4: alpha=0.6, lambda=0.1
## - Fold4: alpha=0.6, lambda=0.1
## + Fold4: alpha=0.7, lambda=0.1
## - Fold4: alpha=0.7, lambda=0.1
## + Fold4: alpha=0.8, lambda=0.1
## - Fold4: alpha=0.8, lambda=0.1
## + Fold4: alpha=0.9, lambda=0.1
## - Fold4: alpha=0.9, lambda=0.1
## + Fold4: alpha=1.0, lambda=0.1
## - Fold4: alpha=1.0, lambda=0.1
## + Fold5: alpha=0.0, lambda=0.1
## - Fold5: alpha=0.0, lambda=0.1
## + Fold5: alpha=0.1, lambda=0.1
## - Fold5: alpha=0.1, lambda=0.1
## + Fold5: alpha=0.2, lambda=0.1
## - Fold5: alpha=0.2, lambda=0.1
## + Fold5: alpha=0.3, lambda=0.1
## - Fold5: alpha=0.3, lambda=0.1
## + Fold5: alpha=0.4, lambda=0.1
## - Fold5: alpha=0.4, lambda=0.1
## + Fold5: alpha=0.5, lambda=0.1
## - Fold5: alpha=0.5, lambda=0.1
## + Fold5: alpha=0.6, lambda=0.1
## - Fold5: alpha=0.6, lambda=0.1
## + Fold5: alpha=0.7, lambda=0.1
## - Fold5: alpha=0.7, lambda=0.1
## + Fold5: alpha=0.8, lambda=0.1
## - Fold5: alpha=0.8, lambda=0.1
## + Fold5: alpha=0.9, lambda=0.1
## - Fold5: alpha=0.9, lambda=0.1
## + Fold5: alpha=1.0, lambda=0.1
## - Fold5: alpha=1.0, lambda=0.1
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.7, lambda = 0 on full training set
print(lrFit) # Print the trained logistic regression model
## glmnet
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3199, 3199, 3201, 3200, 3201
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.0 0.00 0.9477540 0.8150916
## 0.0 0.02 0.9477540 0.8150916
## 0.0 0.04 0.9447531 0.8031191
## 0.0 0.06 0.9395005 0.7812586
## 0.0 0.08 0.9344993 0.7594690
## 0.0 0.10 0.9287493 0.7345168
## 0.1 0.00 0.9637521 0.8786753
## 0.1 0.02 0.9500034 0.8245278
## 0.1 0.04 0.9462515 0.8082264
## 0.1 0.06 0.9400009 0.7822828
## 0.1 0.08 0.9292496 0.7372424
## 0.1 0.10 0.9215030 0.7018912
## 0.2 0.00 0.9635018 0.8779057
## 0.2 0.02 0.9495027 0.8225241
## 0.2 0.04 0.9460009 0.8065513
## 0.2 0.06 0.9367509 0.7690644
## 0.2 0.08 0.9260021 0.7227709
## 0.2 0.10 0.9152527 0.6723897
## 0.3 0.00 0.9637515 0.8788291
## 0.3 0.02 0.9500018 0.8243332
## 0.3 0.04 0.9445027 0.8006202
## 0.3 0.06 0.9340009 0.7572600
## 0.3 0.08 0.9242508 0.7138224
## 0.3 0.10 0.9122533 0.6579847
## 0.4 0.00 0.9640012 0.8797477
## 0.4 0.02 0.9507512 0.8268432
## 0.4 0.04 0.9417530 0.7894936
## 0.4 0.06 0.9327521 0.7516712
## 0.4 0.08 0.9205036 0.6964730
## 0.4 0.10 0.9090027 0.6420841
## 0.5 0.00 0.9640012 0.8797477
## 0.5 0.02 0.9510018 0.8274778
## 0.5 0.04 0.9410027 0.7864150
## 0.5 0.06 0.9302505 0.7403588
## 0.5 0.08 0.9190049 0.6897157
## 0.5 0.10 0.9055027 0.6252131
## 0.6 0.00 0.9640012 0.8797477
## 0.6 0.02 0.9505021 0.8256678
## 0.6 0.04 0.9410024 0.7865206
## 0.6 0.06 0.9287508 0.7334507
## 0.6 0.08 0.9172540 0.6811039
## 0.6 0.10 0.9040027 0.6174021
## 0.7 0.00 0.9642515 0.8806617
## 0.7 0.02 0.9500015 0.8238559
## 0.7 0.04 0.9402527 0.7832490
## 0.7 0.06 0.9270027 0.7255441
## 0.7 0.08 0.9157527 0.6741029
## 0.7 0.10 0.9040018 0.6168488
## 0.8 0.00 0.9642515 0.8806617
## 0.8 0.02 0.9510002 0.8275649
## 0.8 0.04 0.9400027 0.7822849
## 0.8 0.06 0.9242524 0.7135114
## 0.8 0.08 0.9155024 0.6730202
## 0.8 0.10 0.9047524 0.6210541
## 0.9 0.00 0.9642515 0.8806617
## 0.9 0.02 0.9502506 0.8246344
## 0.9 0.04 0.9385015 0.7756153
## 0.9 0.06 0.9245027 0.7146794
## 0.9 0.08 0.9170036 0.6803886
## 0.9 0.10 0.9055018 0.6247090
## 1.0 0.00 0.9642515 0.8806617
## 1.0 0.02 0.9505012 0.8257691
## 1.0 0.04 0.9382521 0.7751520
## 1.0 0.06 0.9250021 0.7172699
## 1.0 0.08 0.9185027 0.6872799
## 1.0 0.10 0.9062521 0.6283877
##
## Kappa was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.7 and lambda = 0.
# Predict using the trained logistic regression model on test data
lrPred = predict(lrFit, test_data)
# Generate confusion matrix for the predictions
cm = confusionMatrix(lrPred, test_data$class)
# Print confusion matrix
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4796 144
## QSO 53 1006
##
## Accuracy : 0.9672
## 95% CI : (0.9623, 0.9715)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8907
##
## Mcnemar's Test P-Value : 1.434e-10
##
## Sensitivity : 0.9891
## Specificity : 0.8748
## Pos Pred Value : 0.9709
## Neg Pred Value : 0.9500
## Prevalence : 0.8083
## Detection Rate : 0.7995
## Detection Prevalence : 0.8235
## Balanced Accuracy : 0.9319
##
## 'Positive' Class : NOT.QSO
##
# Extract Kappa value from the confusion matrix
kappa_value <- cm$overall["Kappa"]
# Extract Accuracy value from the confusion matrix
accuracy_value = cm$overall["Accuracy"]
We have a Kappa value of 0.8907366 and an accuracy of 0.9671612, but it is not the best performance metric because not all the errors have the same impact. It is worst to classify a quasar object as non quasar than viceversa, because we will not get enough money. We are going to use a more conservative threshold chosen manually.
threshold = 0.3
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
cm = confusionMatrix(factor(lrPred), test_data$class)
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4741 110
## QSO 108 1040
##
## Accuracy : 0.9637
## 95% CI : (0.9586, 0.9683)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8827
##
## Mcnemar's Test P-Value : 0.946
##
## Sensitivity : 0.9777
## Specificity : 0.9043
## Pos Pred Value : 0.9773
## Neg Pred Value : 0.9059
## Prevalence : 0.8083
## Detection Rate : 0.7903
## Detection Prevalence : 0.8086
## Balanced Accuracy : 0.9410
##
## 'Positive' Class : NOT.QSO
##
accuracy = cm$overall["Accuracy"]
We have worst accuracy raccuracy`, but we can see the
tradeoff we want between the most impactful error (decreased, as we
wanted) and the other (increased as a payoff).
We are going to use the ROC to compute the optimal threshold for this model, because we do not know if the one we tried is the best one.
roc.lr=roc(test_data$class ~ lrProb[,2])
## Setting levels: control = NOT.QSO, case = QSO
## Setting direction: controls < cases
plot(roc.lr, col="red",print.thres=TRUE)
legend("bottomright", legend=c("lr"), col=c("red"), lwd=2)
auc = roc.lr$auc
auc
## Area under the curve: 0.9784
optimal <- as.numeric(coords(roc.lr, "best", ret = "threshold"))
We can see that the best threshold for our benchmark model is 0.2455102 (close to the one we chose, but not the same) and it is a really good model because the AUC is incredibly high, 0.9783625. Hence, we predict using this threshold.
threshold = optimal
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
cm = confusionMatrix(factor(lrPred), test_data$class)
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4718 100
## QSO 131 1050
##
## Accuracy : 0.9615
## 95% CI : (0.9563, 0.9662)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.877
##
## Mcnemar's Test P-Value : 0.0484
##
## Sensitivity : 0.9730
## Specificity : 0.9130
## Pos Pred Value : 0.9792
## Neg Pred Value : 0.8891
## Prevalence : 0.8083
## Detection Rate : 0.7865
## Detection Prevalence : 0.8031
## Balanced Accuracy : 0.9430
##
## 'Positive' Class : NOT.QSO
##
accuracy = cm$overall["Accuracy"]
For Bayes’ classifiers or logistic regression, variable importance is based on estimated coefficients. Variable importance refers to the measure of the contribution of each predictor variable in a predictive model towards explaining the variability or making accurate predictions. It helps identify which variables have the most significant impact on the model’s performance.
Understanding variable importance helps in feature selection, model interpretation, and identifying which variables have the most influence on the target variable. It can guide data preprocessing efforts and provide insights into the underlying relationships between predictors and the target variable.
lr_imp <- varImp(lrFit, scale = F)
plot(lr_imp, scales = list(y = list(cex = .95)))
redshift is very important for predicting, the most
important variable.
Partial dependence plots are a visualization technique used to understand the relationship between one predictor variable and the target variable in a predictive model. They show how the average prediction of the model changes as the value of one or more predictors varies while keeping all other predictors at fixed values.
These are the marginal effects of one variable after discounting for other variables
partial(lrFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We can see what we saw in the EDA, the higher the value, the bigger
the probability of being a quasar object. Moreover, when the value in
the redshift variable is higher than 2, the object is
always classified as quasar.
However, our goal is not only to obtain the best predictive model, but to optimize the economic profit. We do not want to end up with less money than we need. We take a look at the two possible errors:
We realize that for us, both errors do not have the same impact. The first one is more costly because we will not receive all the money that we need for our research (investigating a quasar object is more expensive in our scenario) and we will not be able to do it fully. We have the following table of profits:
| Prediction/Reference | NOT WSO | QSO |
|---|---|---|
| NOT QSO | 0.25 | -1.0 |
| QSO | -0.10 | 0.25 |
We need to set the profit table as a vector:
profit.unit <- c(0.25, -0.10, -1.0, 0.25)
A naive classifier would classify all the objects as non quasar because it is the most prevalent class (80.82%), so the profit would be:
profit = 0.25*0.81 - 1*0.19 - 0.1*0 + 0.25*0 = 0.2025 - 0.19 = 0.0125 per object.
This profit is what we want to improve by obtaining the best threshold for our model and by taking into account the economic impact.
profit.i = matrix(NA, nrow = 25, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = data.frame(alpha = lrFit$bestTune$alpha, lambda = lrFit$bestTune$lambda)
seq_values <- seq(0.05, 0.45, 0.05)
# Append 0.22 to the sequence
seq_values <- c(seq_values, 0.246)
j <- 0
for (threshold in seq_values){
j <- j + 1
#cat(j)
for(i in 1:25){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
lrFit <- train(class ~ ., data=train, method = "glmnet",
tuneGrid = grid, preProcess = c("center", "scale"),
trControl = trainControl(method = "none", classProbs = TRUE))
lrProb = predict(lrFit, test, type="prob")
lrPred = rep("NOT.QSO", nrow(test))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(lrPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq(0.05,0.5,0.05),col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.2029179 0.2156524 0.2190079 0.2195915 0.2193831 0.2195081 0.2182784
## [8] 0.2184452 0.2157774 0.2203835
We observe that the thresholds around 0.25 are good, so we obtain the best one and compute our final prediction with this model and obtain its economic profit.
indexthr = which.max(medians)
threshold = seq(0.05,0.5,0.05)[indexthr]
lrFit <- train(class ~ ., data=train_data, method = "glmnet",
tuneGrid = data.frame(alpha = 0.7, lambda = 0), preProcess = c("center", "scale"),
trControl = trainControl(method = "none", classProbs = TRUE))
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(lrPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2169028
We can see that we have improved the model immensely, our economic profit is now 0.2169028. However, this is just our benchmark model, the best one yet, but the benchmark nevertheless. We are going to use the machine learning tools we saw in class to try to improve it.
optimal <- as.numeric(coords(roc.lr, “best”, ret = “threshold”)) ### K-Nearest Neighbours (KNN)
K-Nearest Neighbors (KNN) is a simple and intuitive supervised learning algorithm used for classification and regression tasks. It’s a non-parametric method, meaning it doesn’t make any assumptions about the underlying data distribution. Instead, it makes predictions based on the similarity of new data points to existing data points.
KNN’s simplicity and effectiveness make it a popular choice for various machine learning tasks, especially when the dataset is small or the relationships between features and the target variable are non-linear. However, its main drawback is its computational inefficiency, particularly with large datasets, as it requires computing distances between the new data point and all training data points during prediction. Additionally, KNN’s performance can degrade if the feature space has irrelevant or noisy features, or if the data is imbalanced.
As we saw in class, using the package caret is the best
choice, so we decide to compute all the models using this package. We
are going to define a function that will work as our metric in our caret
models, it takes into account the economic nature of our problem.
# We have to add lev = NULL and model = NULL to make caret work
EconomicProfit <- function(data, lev = NULL, model = NULL)
{
y.pred = data$pred
y.true = data$obs
CM = confusionMatrix(y.pred, y.true)$table
out = sum(profit.unit*CM)/sum(CM) # The profit expression
names(out) <- c("EconomicProfit")
out # Important to use this name so caret think it is like accuracy
}
We also need the control function, which performs cross-validation with 5 folds.
ctrl <- trainControl(method = "cv", number = 5,
classProbs = TRUE,
summaryFunction = EconomicProfit,
verbose=F)
Now, we have to train our model.
train_data$class <- factor(train_data$class, labels = make.names(levels(train_data$class)))
knnFit <- train(class ~ .,
method = "knn",
data = train_data,
preProcess = c("center", "scale"),
tuneLength = 7,
metric = "EconomicProfit",
trControl = ctrl)
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## Aggregating results
## Selecting tuning parameters
## Fitting k = 5 on full training set
print(knnFit)
## k-Nearest Neighbors
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3200, 3199, 3201, 3200, 3200
## Resampling results across tuning parameters:
##
## k EconomicProfit
## 5 0.1554340
## 7 0.1471215
## 9 0.1408635
## 11 0.1398742
## 13 0.1341983
## 15 0.1319976
## 17 0.1248487
##
## EconomicProfit was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.
best_hyperparameters <- knnFit$bestTune
profit <- knnFit$results[knnFit$results$k == best_hyperparameters$k, "EconomicProfit"]
We see that of the 7 studied k parameters (5, 7, 9, 11, 13, 15 and 17) the one that maximizes the profit is k = 5, which is chosen by being the one with the highest economic profit (0.155434).
After training the model, we need to obtain its predictions.
knnPred = predict(knnFit, test_data)
cm = confusionMatrix(knnPred,test_data$class)
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4780 362
## QSO 69 788
##
## Accuracy : 0.9282
## 95% CI : (0.9213, 0.9346)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7432
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9858
## Specificity : 0.6852
## Pos Pred Value : 0.9296
## Neg Pred Value : 0.9195
## Prevalence : 0.8083
## Detection Rate : 0.7968
## Detection Prevalence : 0.8571
## Balanced Accuracy : 0.8355
##
## 'Positive' Class : NOT.QSO
##
accuracy = cm$overall["Accuracy"]
profit = EconomicProfit(data = data.frame(pred = knnPred, obs = test_data$class))
profit
## EconomicProfit
## 0.1705451
We obtain a pretty good model, with accuracy 0.9281547 and economic profit 0.1705451. We saw that another way of using knn is making it obtain the predictions based on the probabilities computed using the proportion of votes in the neighbours.
knnProb = predict(knnFit, test_data, type="prob") #prob to get the probabilities instead of the class predictions
head(knnProb) #We get the good and the bad probability (we only need one)
## NOT.QSO QSO
## 1 1.0 0.0
## 2 0.8 0.2
## 3 0.2 0.8
## 4 1.0 0.0
## 5 0.6 0.4
## 6 0.8 0.2
We want to check if the model is better if we change the thresghold manually. Previously, we classified an object as QSO when the probability of being a quasar object was greater than 0.5. Now we are going to classify it as QSO when the probability is greater than 0.2 using the knn model with probabilities we just computed (more conservative because we want to reduce the number of quasar object classified as non quasar).
threshold = 0.2
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.1858476
We have in fact increased slightly the economic profit to 0.1858476, we have a bit better profit. However, we just tried another threshold and it improved, but how do we know if it is the best one? We are going to compute the ROC curve in order to compute this optimal threshold.
roc.knn=roc(test_data$class ~ knnProb[,2])
## Setting levels: control = NOT.QSO, case = QSO
## Setting direction: controls < cases
plot(roc.knn, col="red",print.thres=TRUE, ylim = c(0, 1), xlim = c(1, 0))
optimal <- as.numeric(coords(roc.knn, "best", ret = "threshold"))
From the plot we can see that the best threshold is actually 0.2666667, so with our guess we were closer but not quite there. It is the one with the best balance between sensitivity and specificity. Now, an object is classified as a quasar object if the probability of being QSO is greater than 0.3. Being 0.3 our best threshold instead of 0.5 shows that our classes are unbalanced.
auc = roc.knn$auc
auc
## Area under the curve: 0.9202
We are sure this is a good choice because the are under the curve, as we just computed, is incredibly high 0.9201977, being the maximum 1 and minimum 0 (the higher the better).
knnProb = predict(knnFit, test_data, type="prob")
threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.1858476
However, we can observe that both threshold behave very similarly and we obtain a very similar economic profit, so both thresholds are good choices.
We have fitted a knn model that predicts with class, then we have improved it with one that predicts with probalities. Afterwards, we tried a more conservative threshold which improved it and then using the ROC curve we obtained the optimal threshold that performed as our manually chosen conservative one. When we say improved, we are talking in terms of decreasing the economic cost, not increasing the accuracy (we actually make it worse, we make more errors, but less economically costly). But we still do not know if our manually chosen threshold is the best one, we will compute the best one.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = knnFit$bestTune
seq = c(seq(0.05,0.45,0.05), optimal)
j <- 0
for (threshold in seq){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
knnFit <- train(class ~ .,
method = "knn",
data = train,
preProcess = c("center", "scale"),
tuneLength = 0, # k = 7
tuneGrid = grid,
metric = "EconomicProfit",
trControl = ctrl)
knnProb = predict(knnFit, test, type="prob")
knnPred = rep("NOT.QSO", nrow(test))
knnPred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(knnPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
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## Aggregating results
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## - Fold4: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## - Fold3: k=5
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## - Fold4: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold3: k=5
## + Fold4: k=5
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## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
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## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
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## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
## - Fold2: k=5
## + Fold3: k=5
## - Fold3: k=5
## + Fold4: k=5
## - Fold4: k=5
## + Fold5: k=5
## - Fold5: k=5
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5
## - Fold1: k=5
## + Fold2: k=5
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## Fitting final model on full training set
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq, col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.1476240 0.1491246 0.1477699 0.1643602 0.1637140 0.1655065 0.1650688
## [8] 0.1374531 0.1372447 0.1640892
We observe that the optimal threshold for our model is around 0.3, so we compute the optimal one and make our final prediction using it.
knnProb = predict(knnFit, test_data, type="prob")
indexthr = which.max(medians)
threshold = seq[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.1652859
We are not satisfied with this model, we want to see if we can improve it more. Hence, we try with Support Vector Machines (SVM) models.
We study the variable importance to be able to plot it later.
knn_imp <- varImp(knnFit, scale = F)
plot(knn_imp, scales = list(y = list(cex = .95)))
redshift is once again the most important one.
partial(knnFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(knnFit, pred.var = "z", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(knnFit, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We can see that the bigger the value in either of these 3 variables, the bigger the chance to be a quasar object.
Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for classification and regression tasks. It’s particularly effective in high-dimensional spaces and when the number of features is greater than the number of samples. SVM works by finding the optimal hyperplane that best separates the data points into different classes or predicts continuous outcomes.
SVMs is effective in high-dimensional spaces, versatile because it supports different kernel functions for handling non-linear relationships. and robust against overfitting, especially with proper regularization. However, SVMs can be sensitive to the choice of the kernel function and its parameters. Additionally, they can be computationally expensive, especially with large datasets, and may require careful tuning of hyperparameters for optimal performance
We are going to use caret once again, since we already defined the economic cost (what we want to maximize, our performance metric) and the control function (how we compute the hyper-parameters), we just have to use the train function with the corresponding method and arguments to train our SVM model.
svmFit <- train(class ~., method = "svmRadial",
data = train_data,
preProcess = c("center", "scale"),
tuneGrid = expand.grid(C = c(0.1, 0.25, 0.5, 0.75, 1), # grid for C
sigma = c(0.01, 0.02, 0.05, 0.07, 0.1)), # grid for sigma
metric = "EconomicProfit", # maximizing the profit again
trControl = ctrl)
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## + Fold3: C=0.50, sigma=0.10
## - Fold3: C=0.50, sigma=0.10
## + Fold3: C=0.75, sigma=0.10
## - Fold3: C=0.75, sigma=0.10
## + Fold3: C=1.00, sigma=0.10
## - Fold3: C=1.00, sigma=0.10
## + Fold4: C=0.10, sigma=0.01
## - Fold4: C=0.10, sigma=0.01
## + Fold4: C=0.25, sigma=0.01
## - Fold4: C=0.25, sigma=0.01
## + Fold4: C=0.50, sigma=0.01
## - Fold4: C=0.50, sigma=0.01
## + Fold4: C=0.75, sigma=0.01
## - Fold4: C=0.75, sigma=0.01
## + Fold4: C=1.00, sigma=0.01
## - Fold4: C=1.00, sigma=0.01
## + Fold4: C=0.10, sigma=0.02
## - Fold4: C=0.10, sigma=0.02
## + Fold4: C=0.25, sigma=0.02
## - Fold4: C=0.25, sigma=0.02
## + Fold4: C=0.50, sigma=0.02
## - Fold4: C=0.50, sigma=0.02
## + Fold4: C=0.75, sigma=0.02
## - Fold4: C=0.75, sigma=0.02
## + Fold4: C=1.00, sigma=0.02
## - Fold4: C=1.00, sigma=0.02
## + Fold4: C=0.10, sigma=0.05
## - Fold4: C=0.10, sigma=0.05
## + Fold4: C=0.25, sigma=0.05
## - Fold4: C=0.25, sigma=0.05
## + Fold4: C=0.50, sigma=0.05
## - Fold4: C=0.50, sigma=0.05
## + Fold4: C=0.75, sigma=0.05
## - Fold4: C=0.75, sigma=0.05
## + Fold4: C=1.00, sigma=0.05
## - Fold4: C=1.00, sigma=0.05
## + Fold4: C=0.10, sigma=0.07
## - Fold4: C=0.10, sigma=0.07
## + Fold4: C=0.25, sigma=0.07
## - Fold4: C=0.25, sigma=0.07
## + Fold4: C=0.50, sigma=0.07
## - Fold4: C=0.50, sigma=0.07
## + Fold4: C=0.75, sigma=0.07
## - Fold4: C=0.75, sigma=0.07
## + Fold4: C=1.00, sigma=0.07
## - Fold4: C=1.00, sigma=0.07
## + Fold4: C=0.10, sigma=0.10
## - Fold4: C=0.10, sigma=0.10
## + Fold4: C=0.25, sigma=0.10
## - Fold4: C=0.25, sigma=0.10
## + Fold4: C=0.50, sigma=0.10
## - Fold4: C=0.50, sigma=0.10
## + Fold4: C=0.75, sigma=0.10
## - Fold4: C=0.75, sigma=0.10
## + Fold4: C=1.00, sigma=0.10
## - Fold4: C=1.00, sigma=0.10
## + Fold5: C=0.10, sigma=0.01
## - Fold5: C=0.10, sigma=0.01
## + Fold5: C=0.25, sigma=0.01
## - Fold5: C=0.25, sigma=0.01
## + Fold5: C=0.50, sigma=0.01
## - Fold5: C=0.50, sigma=0.01
## + Fold5: C=0.75, sigma=0.01
## - Fold5: C=0.75, sigma=0.01
## + Fold5: C=1.00, sigma=0.01
## - Fold5: C=1.00, sigma=0.01
## + Fold5: C=0.10, sigma=0.02
## - Fold5: C=0.10, sigma=0.02
## + Fold5: C=0.25, sigma=0.02
## - Fold5: C=0.25, sigma=0.02
## + Fold5: C=0.50, sigma=0.02
## - Fold5: C=0.50, sigma=0.02
## + Fold5: C=0.75, sigma=0.02
## - Fold5: C=0.75, sigma=0.02
## + Fold5: C=1.00, sigma=0.02
## - Fold5: C=1.00, sigma=0.02
## + Fold5: C=0.10, sigma=0.05
## - Fold5: C=0.10, sigma=0.05
## + Fold5: C=0.25, sigma=0.05
## - Fold5: C=0.25, sigma=0.05
## + Fold5: C=0.50, sigma=0.05
## - Fold5: C=0.50, sigma=0.05
## + Fold5: C=0.75, sigma=0.05
## - Fold5: C=0.75, sigma=0.05
## + Fold5: C=1.00, sigma=0.05
## - Fold5: C=1.00, sigma=0.05
## + Fold5: C=0.10, sigma=0.07
## - Fold5: C=0.10, sigma=0.07
## + Fold5: C=0.25, sigma=0.07
## - Fold5: C=0.25, sigma=0.07
## + Fold5: C=0.50, sigma=0.07
## - Fold5: C=0.50, sigma=0.07
## + Fold5: C=0.75, sigma=0.07
## - Fold5: C=0.75, sigma=0.07
## + Fold5: C=1.00, sigma=0.07
## - Fold5: C=1.00, sigma=0.07
## + Fold5: C=0.10, sigma=0.10
## - Fold5: C=0.10, sigma=0.10
## + Fold5: C=0.25, sigma=0.10
## - Fold5: C=0.25, sigma=0.10
## + Fold5: C=0.50, sigma=0.10
## - Fold5: C=0.50, sigma=0.10
## + Fold5: C=0.75, sigma=0.10
## - Fold5: C=0.75, sigma=0.10
## + Fold5: C=1.00, sigma=0.10
## - Fold5: C=1.00, sigma=0.10
## Aggregating results
## Selecting tuning parameters
## Fitting sigma = 0.02, C = 0.75 on full training set
print(svmFit)
## Support Vector Machines with Radial Basis Function Kernel
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3200, 3199, 3201, 3200, 3200
## Resampling results across tuning parameters:
##
## C sigma EconomicProfit
## 0.10 0.01 0.2185235
## 0.10 0.02 0.2189612
## 0.10 0.05 0.2176730
## 0.10 0.07 0.2161739
## 0.10 0.10 0.2143724
## 0.25 0.01 0.2193991
## 0.25 0.02 0.2196109
## 0.25 0.05 0.2196602
## 0.25 0.07 0.2189609
## 0.25 0.10 0.2128101
## 0.50 0.01 0.2204616
## 0.50 0.02 0.2203235
## 0.50 0.05 0.2211235
## 0.50 0.07 0.2196605
## 0.50 0.10 0.2159617
## 0.75 0.01 0.2204986
## 0.75 0.02 0.2212114
## 0.75 0.05 0.2197360
## 0.75 0.07 0.2192986
## 0.75 0.10 0.2166739
## 1.00 0.01 0.2208116
## 1.00 0.02 0.2205365
## 1.00 0.05 0.2199606
## 1.00 0.07 0.2193861
## 1.00 0.10 0.2154602
##
## EconomicProfit was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.02 and C = 0.75.
best_hyperparameters <- svmFit$bestTune
best_row <- svmFit$results[svmFit$results$sigma == best_hyperparameters$sigma & svmFit$results$C == best_hyperparameters$C, ]
profit <- best_row$EconomicProfit
If we look at the economic cost associated with every combination of possible values for our hyper-parameters, we see that the best combination is sigma = 0.02 and c = 0.75 with economic profit 0.2212114. We store the hyperparameters.
svmhyp = best_hyperparameters
After training it and doing hyper-parameter tuning, we obtain the predictions of our test dataset.
svmPred = predict(svmFit, test_data)
head(svmPred)
## [1] NOT.QSO NOT.QSO QSO NOT.QSO NOT.QSO NOT.QSO
## Levels: NOT.QSO QSO
cm = confusionMatrix(svmPred,test_data$class)
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4811 122
## QSO 38 1028
##
## Accuracy : 0.9733
## 95% CI : (0.9689, 0.9773)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9115
##
## Mcnemar's Test P-Value : 5.319e-11
##
## Sensitivity : 0.9922
## Specificity : 0.8939
## Pos Pred Value : 0.9753
## Neg Pred Value : 0.9644
## Prevalence : 0.8083
## Detection Rate : 0.8020
## Detection Prevalence : 0.8223
## Balanced Accuracy : 0.9430
##
## 'Positive' Class : NOT.QSO
##
accuracy = cm$overall["Accuracy"]
profit = EconomicProfit(data = data.frame(pred = svmPred, obs = test_data$class))
profit
## EconomicProfit
## 0.2223621
The performance is really good, the accuracy is 0.9733289 , but, more importantly, the economic profit is 0.2223621, which is the best profit yet, better than all the knn ones we computed and the benchmark too.
As we did in knn, we are going to take another approach and use the probabilities from SVM, which in this case are calibrated using Platt scaling (logistic regression on the SVM’s scores).
svmProb = predict(svmFit, test_data, type="prob")
head(svmProb)
## NOT.QSO QSO
## 1 9.867154e-01 0.01328455
## 2 9.417532e-01 0.05824677
## 3 8.920199e-07 0.99999911
## 4 9.792094e-01 0.02079062
## 5 9.716801e-01 0.02831986
## 6 9.549413e-01 0.04505865
We are going to use this model to compute the predictions changing thresholds. As we did in knn, first we try with the manually chosen 0.2 threshold.
threshold = 0.3
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad
CM = confusionMatrix(factor(Cred.pred), test_data$class)
accuracy = CM$overall["Accuracy"]
profit <- sum(profit.unit*CM$table)/sum(CM$table)
profit
## [1] 0.2240873
It is slightly better again, with an economic profit of 0.2240873 and accuracy of 0.9688281. Once again, we want to look for the optimal threshold, so we use again the ROC.
roc.svm=roc(test_data$class ~ svmProb[,2])
## Setting levels: control = NOT.QSO, case = QSO
## Setting direction: controls < cases
plot(roc.knn, col="red",print.thres=TRUE, ylim = c(0, 1), xlim = c(1, 0)) # ROC curve for knn
plot(roc.svm, add=TRUE, col='blue',print.thres=TRUE) # ROC curve for SVM
legend("bottomright", legend=c("knn", "svm"), col=c("red", "blue"), lwd=2)
optimal <- as.numeric(coords(roc.svm, "best", ret = "threshold"))
Here, we can see that the optimal threshold for SVM with probabilities is 0.2596884. Moreover, and more importantly, since we have plotted both models (knn and svm), we can see that svm is better (we have also seen it in the increase in economic profit) because the area under the curve is bigger, as we can check.
auc = roc.svm$auc
auc
## Area under the curve: 0.9772
Incredibly high, even higher than the knn one, our AUC is now 0.9772102.
So our better model yet is an svm model according to the ROC is the one with threshold 0.2596884. We want to check how much the economic profit improves (we had economic profit = 0.2240873 with threshold 0.2).
threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2247625
In this case, we can see that this ‘optimal’ threshold is actually more or less the same as the 0.2 we tried, but very slightly worse.
We have to keep in mind that the ROC does not take into account the economic aspect of our task, it only takes into account the specificity and the sensitivity. This is why the ‘optimal’ threshold we obtain from it is worse than our chosen one. This is why, as in knn, we are going to compute the optimal threshold taking into account the economic aspect.
profit.i = matrix(NA, nrow = 5, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = best_hyperparameters
seq_values <- seq(0.05, 0.45, 0.05)
# Append 0.22 to the sequence
seq_values <- c(seq_values, optimal)
j <- 0
for (threshold in seq_values){
j <- j + 1
#cat(j)
for(i in 1:5){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
svmFit <- train(class ~., method = "svmRadial",
data = train,
preProcess = c("center", "scale"),
tuneGrid = grid,
metric = "EconomicProfit", # maximizing the profit again
trControl = ctrl)
svmProb = predict(svmFit, test, type="prob")
svmPred = rep("NOT.QSO", nrow(test))
svmPred[which(svmProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(svmPred),test$class)$table
profit = sum(profit.unit*CM)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75
## - Fold1: sigma=0.02, C=0.75
## + Fold2: sigma=0.02, C=0.75
## - Fold2: sigma=0.02, C=0.75
## + Fold3: sigma=0.02, C=0.75
## - Fold3: sigma=0.02, C=0.75
## + Fold4: sigma=0.02, C=0.75
## - Fold4: sigma=0.02, C=0.75
## + Fold5: sigma=0.02, C=0.75
## - Fold5: sigma=0.02, C=0.75
## Aggregating results
## Fitting final model on full training set
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq_values,col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.2097957 0.2213631 0.2239058 0.2238224 0.2221342 0.2221967 0.2242393
## [8] 0.2208212 0.2196749 0.2207795
We compute the optimal threshold and then, we make the final prediction for this model.
svmProb = predict(svmFit, test_data, type="prob")
indexthr = which.max(medians)
threshold = seq_values[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2234289
We have now an economic profit of 0.2234289. We see that it performs worse than with the one of the ROC, but his may be because it actually is not better but by chance in this exact chance it is. However, doing cross-validation in the loop we see that the one that performs better on the average is 0.35.
We store its profit and threshold.
svmprofit = profit
svmoptimal = threshold
We study which variables are the most influencial in our prediction.
svm_imp <- varImp(svmFit, scale = F)
plot(svm_imp, scales = list(y = list(cex = .95)))
Once again,
redshift is the most influential one followed
by zand i.
partial(svmFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(svmFit, pred.var = "z", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(svmFit, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We can see what we saw in the EDA and on the penalized logistic
regression model, the higher the value, the bigger the probability of
being a quasar object. Moreover, when the value in the
redshift variable is higher than 2, the object is always
classified as quasar. Moreover, we can see that both variables
zand i have the same dependency with `class.
The higher their value, the higher the probability that the object is
classified as quasar object.
Decision Trees are versatile and powerful supervised machine learning algorithms used for both classification and regression tasks. They create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
Decision trees are easy to interpret and visualize, they have the ability to handle both numerical and categorical data without the need for feature scaling and they are robustness to outliers. However, decision trees can suffer from overfitting, particularly when the tree grows too deep and captures noise in the training data. Ensemble methods like Random Forests and Gradient Boosting Machines are often used to mitigate this issue by combining multiple decision trees to improve predictive performance, we will implement them later on.
We are going to use the function available in the caret package again. We first fit the model with our economic profit condition.
grid_c50 <- expand.grid( .winnow = c(TRUE,FALSE), .trials=c(1,5,10,15,20), .model="tree" )
fit.c50 <- train(class ~.,
data=train_data,
method="C5.0",
metric="EconomicProfit",
tuneGrid = grid_c50,
trControl = ctrl)
## + Fold1: model=tree, winnow=FALSE, trials=20
## - Fold1: model=tree, winnow=FALSE, trials=20
## + Fold1: model=tree, winnow= TRUE, trials=20
## - Fold1: model=tree, winnow= TRUE, trials=20
## + Fold2: model=tree, winnow=FALSE, trials=20
## - Fold2: model=tree, winnow=FALSE, trials=20
## + Fold2: model=tree, winnow= TRUE, trials=20
## - Fold2: model=tree, winnow= TRUE, trials=20
## + Fold3: model=tree, winnow=FALSE, trials=20
## - Fold3: model=tree, winnow=FALSE, trials=20
## + Fold3: model=tree, winnow= TRUE, trials=20
## - Fold3: model=tree, winnow= TRUE, trials=20
## + Fold4: model=tree, winnow=FALSE, trials=20
## - Fold4: model=tree, winnow=FALSE, trials=20
## + Fold4: model=tree, winnow= TRUE, trials=20
## - Fold4: model=tree, winnow= TRUE, trials=20
## + Fold5: model=tree, winnow=FALSE, trials=20
## - Fold5: model=tree, winnow=FALSE, trials=20
## + Fold5: model=tree, winnow= TRUE, trials=20
## - Fold5: model=tree, winnow= TRUE, trials=20
## Aggregating results
## Selecting tuning parameters
## Fitting trials = 10, model = tree, winnow = TRUE on full training set
fit.c50
## C5.0
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3200, 3200, 3200, 3200, 3200
## Resampling results across tuning parameters:
##
## winnow trials EconomicProfit
## FALSE 1 0.2205750
## FALSE 5 0.2229375
## FALSE 10 0.2215500
## FALSE 15 0.2217625
## FALSE 20 0.2228375
## TRUE 1 0.2178375
## TRUE 5 0.2196875
## TRUE 10 0.2239875
## TRUE 15 0.2231375
## TRUE 20 0.2237125
##
## Tuning parameter 'model' was held constant at a value of tree
## EconomicProfit was used to select the optimal model using the largest value.
## The final values used for the model were trials = 10, model = tree and winnow
## = TRUE.
best_hyperparameters <- fit.c50$bestTune
best_row <- fit.c50$results[fit.c50$results$winnow == best_hyperparameters$winnow &
fit.c50$results$trials == best_hyperparameters$trials &
fit.c50$results$model == best_hyperparameters$model, ]
profit <- best_row$EconomicProfit
Once again, we look at all the combination and their resulting economic profit to choose the combination with the highest economic profit, which is 0.2239875 with winnow = TRUE and 10 trials. We store their values.
dthyp = best_hyperparameters
we are going to take a visual look at our tree with the summary function.
summary(fit.c50)
##
## Call:
## (function (x, y, trials = 1, rules = FALSE, weights = NULL, control
## = TRUE, noGlobalPruning = FALSE, CF = 0.25, minCases = 2, fuzzyThreshold
## = FALSE, sample = 0, earlyStopping = TRUE, label = "outcome", seed = 592L))
##
##
## C5.0 [Release 2.07 GPL Edition] Wed Mar 13 23:04:19 2024
## -------------------------------
##
## Class specified by attribute `outcome'
##
## Read 4000 cases (27 attributes) from undefined.data
##
## 6 attributes winnowed
## Estimated importance of remaining attributes:
##
## 638% redshift
## 46% u
## 24% g
## 14% spec_obj_ID
## 2% z
## 2% field_ID
## <1% alpha
## <1% delta
## <1% r
## <1% i
## <1% run_ID1000 - 2000
## <1% run_ID2000 - 3000
## <1% run_ID3000 - 4000
## <1% run_ID5000 - 6000
## <1% run_ID7000 - 8000
## <1% run_ID8000 - 9000
## <1% cam_col3
## <1% cam_col4
## <1% cam_col5
## <1% cam_col6
## <1% MJD
##
## ----- Trial 0: -----
##
## Decision tree:
##
## redshift > 0.9874022:
## :...g <= 22.3466: QSO (591/12)
## : g > 22.3466:
## : :...redshift > 1.726004: QSO (17)
## : redshift <= 1.726004:
## : :...spec_obj_ID <= 9.619727e+18: QSO (13/2)
## : spec_obj_ID > 9.619727e+18: NOT.QSO (32/6)
## redshift <= 0.9874022:
## :...redshift <= 0.6092985:
## :...u <= 19.74477:
## : :...redshift <= 0.1674231: NOT.QSO (599/1)
## : : redshift > 0.1674231:
## : : :...redshift <= 0.2514768: NOT.QSO (12/5)
## : : redshift > 0.2514768: QSO (13)
## : u > 19.74477:
## : :...z <= 19.62704: NOT.QSO (1703/14)
## : z > 19.62704:
## : :...redshift <= 0.07216766: NOT.QSO (244)
## : redshift > 0.07216766:
## : :...g > 21.06557: NOT.QSO (139/17)
## : g <= 21.06557:
## : :...redshift <= 0.3640681: NOT.QSO (3)
## : redshift > 0.3640681: QSO (7)
## redshift > 0.6092985:
## :...u > 21.93472: NOT.QSO (542/30)
## u <= 21.93472:
## :...g > 21.76308: NOT.QSO (7)
## g <= 21.76308:
## :...run_ID7000 - 8000 <= 0: QSO (62/5)
## run_ID7000 - 8000 > 0:
## :...z <= 19.47418: NOT.QSO (4)
## z > 19.47418:
## :...r <= 21.20465: QSO (10)
## r > 21.20465: NOT.QSO (2)
##
## ----- Trial 1: -----
##
## Decision tree:
##
## redshift <= 0.786133:
## :...i > 20.97315:
## : :...redshift <= 0.07216766: NOT.QSO (71.8)
## : : redshift > 0.07216766: QSO (237.8/65.8)
## : i <= 20.97315:
## : :...redshift <= 0.1674231: NOT.QSO (1037.9/11.4)
## : redshift > 0.1674231:
## : :...u <= 21.04776: QSO (223.4/52.9)
## : u > 21.04776:
## : :...i <= 18.86458: NOT.QSO (300.1)
## : i > 18.86458:
## : :...redshift <= 0.2363302: QSO (40.2/6)
## : redshift > 0.2363302:
## : :...u > 22.80355: NOT.QSO (641.9/45.5)
## : u <= 22.80355:
## : :...z <= 19.43995: NOT.QSO (181.6/24.3)
## : z > 19.43995: QSO (161.3/46.1)
## redshift > 0.786133:
## :...redshift > 1.638417: QSO (301.6/0.8)
## redshift <= 1.638417:
## :...u <= 21.56305: QSO (119.4)
## u > 21.56305:
## :...spec_obj_ID <= 8.147113e+18: QSO (117.4/5.3)
## spec_obj_ID > 8.147113e+18:
## :...spec_obj_ID > 1.209107e+19: QSO (77.3/8.3)
## spec_obj_ID <= 1.209107e+19:
## :...run_ID5000 - 6000 > 0: NOT.QSO (50.7)
## run_ID5000 - 6000 <= 0:
## :...run_ID7000 - 8000 > 0: NOT.QSO (116.6/15.9)
## run_ID7000 - 8000 <= 0:
## :...u <= 25.8097: QSO (290/126.4)
## u > 25.8097: NOT.QSO (31.1/0.8)
##
## ----- Trial 2: -----
##
## Decision tree:
##
## redshift > 0.8952028:
## :...redshift > 1.667782: QSO (220.1)
## : redshift <= 1.667782:
## : :...g <= 21.14217: QSO (64.7)
## : g > 21.14217:
## : :...g > 24.25772: NOT.QSO (26.4/0.6)
## : g <= 24.25772:
## : :...MJD > 58252: QSO (31.8)
## : MJD <= 58252:
## : :...MJD <= 57015: QSO (118.2/35.7)
## : MJD > 57015: NOT.QSO (348.5/131.9)
## redshift <= 0.8952028:
## :...redshift <= 0.02193579: NOT.QSO (542.6)
## redshift > 0.02193579:
## :...alpha > 358.1465: QSO (49.6/13.2)
## alpha <= 358.1465:
## :...i <= 17.28261: NOT.QSO (431.2/9.4)
## i > 17.28261:
## :...u > 23.83094: NOT.QSO (587.3/40.3)
## u <= 23.83094:
## :...z > 22.34539: NOT.QSO (41.4)
## z <= 22.34539:
## :...redshift <= 0.1263195: NOT.QSO (36.7)
## redshift > 0.1263195:
## :...u <= 21.18736: QSO (218.9/85.4)
## u > 21.18736:
## :...z <= 18.468: NOT.QSO (169.7)
## z > 18.468:
## :...u > 23.7849: QSO (43.7/7.3)
## u <= 23.7849:
## :...spec_obj_ID > 1.228137e+19: QSO (77.7/22.9)
## spec_obj_ID <= 1.228137e+19:
## :...z <= 19.97678: NOT.QSO (537.4/102.1)
## z > 19.97678:
## :...spec_obj_ID <= 8.147113e+18: QSO (120.6/23.9)
## spec_obj_ID > 8.147113e+18: NOT.QSO (333.5/75.5)
##
## ----- Trial 3: -----
##
## Decision tree:
##
## redshift <= 0.8710588:
## :...redshift <= 0.02193579: NOT.QSO (428.5)
## : redshift > 0.02193579:
## : :...z <= 18.46682:
## : :...redshift <= 0.1674231: NOT.QSO (314)
## : : redshift > 0.1674231:
## : : :...u <= 19.68214: QSO (52.5/10)
## : : u > 19.68214: NOT.QSO (438.9/21)
## : z > 18.46682:
## : :...i > 22.2247: QSO (46.4/10)
## : i <= 22.2247:
## : :...u > 22.87833: NOT.QSO (786.6/94.9)
## : u <= 22.87833:
## : :...z > 21.5214: NOT.QSO (56.9)
## : z <= 21.5214:
## : :...delta <= 25.62602:
## : :...i <= 21.11553: NOT.QSO (353.3/82.1)
## : : i > 21.11553: QSO (113.2/40)
## : delta > 25.62602:
## : :...g > 22.62975: NOT.QSO (39.7)
## : g <= 22.62975:
## : :...delta <= 52.41642: QSO (314.7/77.7)
## : delta > 52.41642: NOT.QSO (56.7/14)
## redshift > 0.8710588:
## :...u <= 22.09596: QSO (391.5/19.8)
## u > 22.09596:
## :...redshift > 1.638417: QSO (80.6)
## redshift <= 1.638417:
## :...alpha > 341.0215: NOT.QSO (34.3/0.9)
## alpha <= 341.0215:
## :...alpha > 336.5887: QSO (28.6)
## alpha <= 336.5887:
## :...redshift > 1.556532: NOT.QSO (22.7/0.9)
## redshift <= 1.556532:
## :...MJD > 58158: QSO (70.9/8.4)
## MJD <= 58158:
## :...u <= 22.21124: QSO (17.8)
## u > 22.21124:
## :...r > 22.70459: NOT.QSO (18.6)
## r <= 22.70459:
## :...r <= 22.58526: NOT.QSO (304.1/115.3)
## r > 22.58526: QSO (29.7)
##
## ----- Trial 4: -----
##
## Decision tree:
##
## redshift > 0.9408078:
## :...spec_obj_ID <= 9.865324e+18: QSO (528.1/50.6)
## : spec_obj_ID > 9.865324e+18:
## : :...MJD <= 57938: NOT.QSO (98.2/18.6)
## : MJD > 57938: QSO (223.5/55.9)
## redshift <= 0.9408078:
## :...redshift <= 0.02193579: NOT.QSO (336.5)
## redshift > 0.02193579:
## :...u > 25.75616: NOT.QSO (99.9)
## u <= 25.75616:
## :...u > 25.61604: QSO (58.1/11.7)
## u <= 25.61604:
## :...r <= 17.83108: NOT.QSO (300.9/12.9)
## r > 17.83108:
## :...g > 21.64636:
## :...g > 24.65492: QSO (54.5/19.4)
## : g <= 24.65492:
## : :...z <= 19.37151: NOT.QSO (258.7)
## : z > 19.37151:
## : :...u > 23.78781: NOT.QSO (166.2)
## : u <= 23.78781:
## : :...u > 23.75353: QSO (28.3/2.7)
## : u <= 23.75353:
## : :...i <= 22.17304: NOT.QSO (656.2/142.4)
## : i > 22.17304: QSO (25.8/6.7)
## g <= 21.64636:
## :...redshift > 0.6401274: QSO (295.3/63)
## redshift <= 0.6401274:
## :...u > 23.8334: NOT.QSO (50.3)
## u <= 23.8334:
## :...u > 23.80195: QSO (29/3.4)
## u <= 23.80195:
## :...run_ID8000 - 9000 > 0: QSO (47.8/16.6)
## run_ID8000 - 9000 <= 0:
## :...alpha > 357.2946: QSO (38.6/9.1)
## alpha <= 357.2946:
## :...redshift <= 0.1703892: NOT.QSO (102.9)
## redshift > 0.1703892:
## :...g <= 19.24383: QSO (52/17.1)
## g > 19.24383:
## :...z <= 18.46494: NOT.QSO (152.5/5.9)
## z > 18.46494:
## :...z <= 18.47387: QSO (20.7/0.4)
## z > 18.47387: NOT.QSO (376/113.4)
##
## ----- Trial 5: -----
##
## Decision tree:
##
## redshift > 0.9408078:
## :...redshift > 1.667782: QSO (167.2)
## : redshift <= 1.667782:
## : :...r <= 21.80325: QSO (450.1/97.5)
## : r > 21.80325: NOT.QSO (264/101.9)
## redshift <= 0.9408078:
## :...redshift <= 0.02193579: NOT.QSO (265.8)
## redshift > 0.02193579:
## :...z <= 19.45234:
## :...u > 23.83094: NOT.QSO (238.5)
## : u <= 23.83094:
## : :...u <= 18.01646: QSO (33/10.4)
## : u > 18.01646:
## : :...i <= 17.28261: NOT.QSO (233.9)
## : i > 17.28261:
## : :...run_ID7000 - 8000 > 0: NOT.QSO (68.6)
## : run_ID7000 - 8000 <= 0:
## : :...redshift <= 0.1696456: NOT.QSO (71.3)
## : redshift > 0.1696456:
## : :...u <= 21.16582: QSO (212.3/89.5)
## : u > 21.16582: NOT.QSO (545/92.3)
## z > 19.45234:
## :...g <= 21.08929: QSO (174.2/16.1)
## g > 21.08929:
## :...g > 22.24555:
## :...redshift <= 0.1810932: QSO (34.3/6.6)
## : redshift > 0.1810932:
## : :...delta <= -4.478744: QSO (33.9/11.1)
## : delta > -4.478744: NOT.QSO (557.8/80)
## g <= 22.24555:
## :...r > 21.92936: NOT.QSO (87.4/8.6)
## r <= 21.92936:
## :...r > 21.71312: QSO (58.2/1.2)
## r <= 21.71312:
## :...alpha > 247.0963: QSO (110.9/23.7)
## alpha <= 247.0963:
## :...redshift <= 0.4894672: NOT.QSO (79)
## redshift > 0.4894672:
## :...redshift <= 0.538325: QSO (42.9/0.9)
## redshift > 0.538325: NOT.QSO (272/103)
##
## ----- Trial 6: -----
##
## Decision tree:
##
## z <= 18.72262:
## :...redshift <= 0.1674231: NOT.QSO (431.3)
## : redshift > 0.1674231:
## : :...u <= 19.68214: QSO (74.8/16)
## : u > 19.68214: NOT.QSO (530.7/43.8)
## z > 18.72262:
## :...redshift > 1.263215:
## :...i <= 22.25686: QSO (384.8/45.9)
## : i > 22.25686: NOT.QSO (28.3/4.9)
## redshift <= 1.263215:
## :...redshift <= 0.01859772: NOT.QSO (84.2)
## redshift > 0.01859772:
## :...u <= 22.24435:
## :...redshift <= 0.3422371: NOT.QSO (102.3/16.2)
## : redshift > 0.3422371:
## : :...u <= 21.54826: QSO (243.2/21.7)
## : u > 21.54826:
## : :...i <= 19.64818: NOT.QSO (29.4)
## : i > 19.64818:
## : :...g > 22.69906: NOT.QSO (21)
## : g <= 22.69906:
## : :...u <= 21.58492: NOT.QSO (29.6/2.1)
## : u > 21.58492:
## : :...run_ID2000 - 3000 <= 0: QSO (303.2/81.2)
## : run_ID2000 - 3000 > 0: NOT.QSO (33.1/10.5)
## u > 22.24435:
## :...r <= 20.01771: QSO (45.6/8.3)
## r > 20.01771:
## :...run_ID1000 - 2000 > 0: NOT.QSO (57.6)
## run_ID1000 - 2000 <= 0:
## :...run_ID8000 - 9000 > 0: NOT.QSO (123/3.5)
## run_ID8000 - 9000 <= 0:
## :...delta > 50.22329: QSO (141.7/46.8)
## delta <= 50.22329:
## :...spec_obj_ID <= 4.215504e+18: QSO (48/9.5)
## spec_obj_ID > 4.215504e+18:
## :...i <= 20.54975: NOT.QSO (458.4/37.4)
## i > 20.54975:
## :...z > 22.51591: NOT.QSO (49)
## z <= 22.51591:
## :...spec_obj_ID <= 8.863335e+18: QSO (203.9/57.9)
## spec_obj_ID > 8.863335e+18:
## :...z > 22.14288: QSO (48.8/10.2)
## z <= 22.14288:
## :...delta > 40.83349: QSO (31.1/4.4)
## delta <= 40.83349: [S1]
##
## SubTree [S1]
##
## spec_obj_ID <= 1.283202e+19: NOT.QSO (465.4/91.1)
## spec_obj_ID > 1.283202e+19: QSO (31.6/6.6)
##
## ----- Trial 7: -----
##
## Decision tree:
##
## redshift > 0.9787375:
## :...redshift > 1.667782: QSO (145.4)
## : redshift <= 1.667782:
## : :...spec_obj_ID <= 8.209184e+18: QSO (70)
## : spec_obj_ID > 8.209184e+18:
## : :...g <= 21.35246: QSO (88.7/8.2)
## : g > 21.35246:
## : :...delta > 48.92759: NOT.QSO (68.4/7.6)
## : delta <= 48.92759:
## : :...delta <= 32.23484: NOT.QSO (288.8/120.5)
## : delta > 32.23484: QSO (107)
## redshift <= 0.9787375:
## :...z > 19.8615:
## :...redshift <= 0.01859772: NOT.QSO (38.1)
## : redshift > 0.01859772:
## : :...z > 22.17026: NOT.QSO (93.6/4.7)
## : z <= 22.17026:
## : :...MJD > 58258: QSO (80.8/17.1)
## : MJD <= 58258:
## : :...alpha <= 14.67885: NOT.QSO (60.6/4.5)
## : alpha > 14.67885:
## : :...spec_obj_ID > 1.077716e+19: NOT.QSO (111.4/10.8)
## : spec_obj_ID <= 1.077716e+19:
## : :...MJD > 58069: NOT.QSO (33.2)
## : MJD <= 58069:
## : :...MJD <= 54508: NOT.QSO (29.3)
## : MJD > 54508:
## : :...delta > 56.74844: QSO (34.1/3.5)
## : delta <= 56.74844:
## : :...delta > 52.91341: NOT.QSO (39.5/2.1)
## : delta <= 52.91341:
## : :...alpha <= 19.40138: QSO (50.5/7.2)
## : alpha > 19.40138:
## : :...delta > 49.32496: QSO (52/8.1)
## : delta <= 49.32496: [S1]
## z <= 19.8615:
## :...u > 23.83094: NOT.QSO (427.1)
## u <= 23.83094:
## :...redshift <= 0.1674231: NOT.QSO (359.4)
## redshift > 0.1674231:
## :...alpha > 344.8588: NOT.QSO (84.4)
## alpha <= 344.8588:
## :...i > 20.22575: NOT.QSO (75)
## i <= 20.22575:
## :...redshift > 0.7549464: QSO (33.9/8)
## redshift <= 0.7549464:
## :...u > 23.7849: QSO (43.6/12.4)
## u <= 23.7849:
## :...g > 22.02032: NOT.QSO (165.9)
## g <= 22.02032:
## :...u > 22.80464: NOT.QSO (81.8)
## u <= 22.80464:
## :...u > 22.7181: QSO (51.1/15.3)
## u <= 22.7181:
## :...u > 22.45663: NOT.QSO (63)
## u <= 22.45663:
## :...u > 22.4217: QSO (37/7.6)
## u <= 22.4217:
## :...u > 22.21124: NOT.QSO (53.4)
## u <= 22.21124:
## :...z > 19.41521: QSO (102.6/25.4)
## z <= 19.41521: [S2]
##
## SubTree [S1]
##
## cam_col6 > 0: NOT.QSO (31.4/0.2)
## cam_col6 <= 0:
## :...spec_obj_ID <= 8.147113e+18: QSO (184.6/59.3)
## spec_obj_ID > 8.147113e+18: NOT.QSO (382.9/147.2)
##
## SubTree [S2]
##
## u > 21.16444: NOT.QSO (109.3)
## u <= 21.16444:
## :...redshift <= 0.534772: NOT.QSO (302.3/92.2)
## redshift > 0.534772: QSO (18.7)
##
## ----- Trial 8: -----
##
## Decision tree:
##
## redshift <= 0.8952028:
## :...u > 22.05367:
## : :...g > 25.1582: QSO (52.4/22.8)
## : : g <= 25.1582:
## : : :...redshift <= 0.2390244: NOT.QSO (198.3/68.6)
## : : redshift > 0.2390244:
## : : :...g > 22.97449: NOT.QSO (318)
## : : g <= 22.97449:
## : : :...i <= 20.52125: NOT.QSO (899/55.5)
## : : i > 20.52125:
## : : :...spec_obj_ID <= 8.147113e+18: QSO (70.7/13.7)
## : : spec_obj_ID > 8.147113e+18: NOT.QSO (238.2/47.3)
## : u <= 22.05367:
## : :...u > 22.02661: QSO (36.1/0.4)
## : u <= 22.02661:
## : :...u > 21.93472: NOT.QSO (83.6)
## : u <= 21.93472:
## : :...redshift <= 0.1674231: NOT.QSO (308.3/10.1)
## : redshift > 0.1674231:
## : :...u <= 19.15346: QSO (55.8)
## : u > 19.15346:
## : :...i <= 17.28261: NOT.QSO (71.6)
## : i > 17.28261:
## : :...g > 21.76308: NOT.QSO (26)
## : g <= 21.76308:
## : :...redshift > 0.7179198: QSO (110.5/6.7)
## : redshift <= 0.7179198:
## : :...delta <= 1.19691: NOT.QSO (97.5/16.2)
## : delta > 1.19691:
## : :...delta <= 2.444826: QSO (26/0.1)
## : delta > 2.444826: [S1]
## redshift > 0.8952028:
## :...redshift > 1.667782: QSO (116.2)
## redshift <= 1.667782:
## :...u <= 22.04539: QSO (264.6/25.2)
## u > 22.04539:
## :...spec_obj_ID > 1.178267e+19: QSO (85.3/8.5)
## spec_obj_ID <= 1.178267e+19:
## :...run_ID5000 - 6000 > 0: NOT.QSO (39.3/3.4)
## run_ID5000 - 6000 <= 0:
## :...spec_obj_ID > 1.174672e+19: NOT.QSO (26.4)
## spec_obj_ID <= 1.174672e+19:
## :...i > 22.52137: QSO (32.3)
## i <= 22.52137:
## :...spec_obj_ID > 1.063662e+19: QSO (34.7/0.8)
## spec_obj_ID <= 1.063662e+19:
## :...spec_obj_ID > 1.049904e+19: NOT.QSO (52.1)
## spec_obj_ID <= 1.049904e+19:
## :...r > 22.19364: NOT.QSO (46)
## r <= 22.19364:
## :...z <= 19.65414: NOT.QSO (17.3/1.9)
## z > 19.65414:
## :...delta <= 50.90433: QSO (257.4/45)
## delta > 50.90433: NOT.QSO (68.8/20.6)
##
## SubTree [S1]
##
## run_ID1000 - 2000 <= 0: QSO (345.1/145.2)
## run_ID1000 - 2000 > 0: NOT.QSO (18.5)
##
## ----- Trial 9: -----
##
## Decision tree:
##
## redshift > 0.9787375:
## :...spec_obj_ID <= 8.457806e+18: QSO (161.2)
## : spec_obj_ID > 8.457806e+18:
## : :...u <= 22.357: QSO (337.5/44.5)
## : u > 22.357: NOT.QSO (246.3/107.3)
## redshift <= 0.9787375:
## :...z <= 19.97678:
## :...u > 22.75362: NOT.QSO (727.5/6.7)
## : u <= 22.75362:
## : :...u > 22.75153: QSO (27.3)
## : u <= 22.75153:
## : :...redshift <= 0.1674231: NOT.QSO (240.3)
## : redshift > 0.1674231:
## : :...g <= 17.89093: QSO (25)
## : g > 17.89093: NOT.QSO (1117.4/213.3)
## z > 19.97678:
## :...z > 22.3164: NOT.QSO (59.7)
## z <= 22.3164:
## :...u > 25.76417: NOT.QSO (39.9)
## u <= 25.76417:
## :...u > 25.68847: QSO (29.3)
## u <= 25.68847:
## :...g > 22.24555: NOT.QSO (303.9/59.4)
## g <= 22.24555:
## :...redshift <= 0.2236984: NOT.QSO (89.5/6.6)
## redshift > 0.2236984:
## :...redshift <= 0.2390244: QSO (33.5)
## redshift > 0.2390244:
## :...r > 21.92936: NOT.QSO (62.6)
## r <= 21.92936:
## :...redshift <= 0.4134484: NOT.QSO (37.7/3.5)
## redshift > 0.4134484: QSO (447.4/130.6)
##
##
## Evaluation on training data (4000 cases):
##
## Trial Decision Tree
## ----- ----------------
## Size Errors
##
## 0 18 92( 2.3%)
## 1 17 331( 8.3%)
## 2 19 221( 5.5%)
## 3 22 195( 4.9%)
## 4 23 225( 5.6%)
## 5 21 171( 4.3%)
## 6 25 228( 5.7%)
## 7 36 180( 4.5%)
## 8 29 148( 3.7%)
## 9 17 180( 4.5%)
## boost 34( 0.8%) <<
##
##
## (a) (b) <-classified as
## ---- ----
## 3226 7 (a): class NOT.QSO
## 27 740 (b): class QSO
##
##
## Attribute usage:
##
## 100.00% u
## 100.00% z
## 100.00% redshift
## 97.68% i
## 76.22% g
## 66.32% r
## 62.88% alpha
## 46.55% spec_obj_ID
## 39.78% run_ID8000 - 9000
## 36.13% delta
## 31.45% run_ID1000 - 2000
## 26.45% run_ID7000 - 8000
## 16.80% MJD
## 7.80% run_ID5000 - 6000
## 4.68% cam_col6
## 1.92% run_ID2000 - 3000
##
##
## Time: 0.5 secs
Now that we have our model trained, we want to predict our test dataset.
c50.pred <- predict(fit.c50, newdata=test_data)
cm = confusionMatrix(c50.pred, test_data$class)
cm
## Confusion Matrix and Statistics
##
## Reference
## Prediction NOT.QSO QSO
## NOT.QSO 4798 102
## QSO 51 1048
##
## Accuracy : 0.9745
## 95% CI : (0.9702, 0.9783)
## No Information Rate : 0.8083
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9163
##
## Mcnemar's Test P-Value : 5.294e-05
##
## Sensitivity : 0.9895
## Specificity : 0.9113
## Pos Pred Value : 0.9792
## Neg Pred Value : 0.9536
## Prevalence : 0.8083
## Detection Rate : 0.7998
## Detection Prevalence : 0.8168
## Balanced Accuracy : 0.9504
##
## 'Positive' Class : NOT.QSO
##
accuracy = cm$overall["Accuracy"]
profit = EconomicProfit(data = data.frame(pred = c50.pred, obs = test_data$class))
profit
## EconomicProfit
## 0.225771
In terms of accuracy, it is a really strong model with 0.9744957, but we are interested in the economic profit. In this model, the economic profit is 0.225771, which does improve the economic of our best SVM model.
We are, once again, computing the probabilities (fraction of samples of the same class in a leaf) in order to be able to change the threshold manually and try to improve more our model. We once again try with 0.2 as the threshold.
c50.Prob = predict(fit.c50, test_data, type="prob")
head(c50.Prob)
## NOT.QSO QSO
## 2370 1.0000000 0.00000000
## 5274 0.9129324 0.08706757
## 9291 0.0000000 1.00000000
## 8827 1.0000000 0.00000000
## 356 0.9263597 0.07364031
## 7701 1.0000000 0.00000000
threshold = 0.2
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2227788
With this threshold it is actually a bit worse, the economic profit is lower, 0.2227788. We will try with a different threshold, a little higher.
threshold = 0.3
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)
accuracy = CM$overall["Accuracy"]
profit <- sum(profit.unit*CM$table)/sum(CM$table)
profit
## [1] 0.2254709
This does actually improve, the economic profit is 0.2254709 and the accuracy is 0.9594932. We are going to follow the same structure as before, so we are going to compute the ROC, but include the 2 previous models to compare them too.
roc.dt=roc(test_data$class ~ c50.Prob[,2])
## Setting levels: control = NOT.QSO, case = QSO
## Setting direction: controls < cases
plot(roc.knn, col="red",print.thres=TRUE)
plot(roc.svm, add=TRUE, col='blue',print.thres=TRUE)
plot(roc.dt, add=TRUE, col='green',print.thres=TRUE)
legend("bottomright", legend=c("knn", "svm", "dt"), col=c("red", "blue", "green"), lwd=2)
optimal <- as.numeric(coords(roc.dt, "best", ret = "threshold"))
auc = roc.dt$auc
We plotted the three model seen until now (knn, svm and decision trees) and we can see that the best is decision trees followed by svm and then, the worst, knn. The optimal threshold computed by the ROC for our decision tree model is 0.3655297 and the resulting AUC is 0.9813301, which is an incredibly good model. As seen before, maybe this ‘optimal’ threshold is not optimal according to our economic profit, so we are going to check if it improves or not.
c50.Prob = predict(fit.c50, test_data, type="prob")
threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2274879
In this case, it actually does improve the economic profit to 0.2274879. From all the models explored until now, the best performing one yet, in terms of economic profit, is the decision tree with probabilities with 0.3655297 as the threshold. However, we do not know if it the optimal according to our economic aspect, because the ROC does not take it into account as we previously said. We compute the optimal and make the final prediction.
Since the ROC does not take into account the economic nature of our problem and we compute the optimal threshold with the following loop, we are not going to keep using the ROC.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid <- best_hyperparameters
seq_values <- seq(0.05, 0.45, 0.05)
# Append 0.22 to the sequence
seq_values <- c(seq_values, optimal)
j <- 0
for (threshold in seq_values){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
fit.c50 <- train(class ~.,
data=train,
method="C5.0",
metric="EconomicProfit",
tuneGrid = grid,
trControl = ctrl)
dtProb = predict(fit.c50, test, type="prob")
dtPred = rep("NOT.QSO", nrow(test))
dtPred[which(dtProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dtPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE
## - Fold1: trials=10, model=tree, winnow=TRUE
## + Fold2: trials=10, model=tree, winnow=TRUE
## - Fold2: trials=10, model=tree, winnow=TRUE
## + Fold3: trials=10, model=tree, winnow=TRUE
## - Fold3: trials=10, model=tree, winnow=TRUE
## + Fold4: trials=10, model=tree, winnow=TRUE
## - Fold4: trials=10, model=tree, winnow=TRUE
## + Fold5: trials=10, model=tree, winnow=TRUE
## - Fold5: trials=10, model=tree, winnow=TRUE
## Aggregating results
## Fitting final model on full training set
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# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq_values, col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.1541476 0.2042726 0.2092330 0.2191747 0.2224260 0.2238224 0.2244894
## [8] 0.2229471 0.2229054 0.2231138
The optimal threshold is computed and then, we obtain the final predictions and corresponding economic profit.
indexthr = which.max(medians)
threshold = seq_values[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2267878
This is our best model to this moment, but we are not satisfied yet, we know a bunch of more models with machine learning, so we are going to study them too, random forest first. We store its profit and optimal threshold too.
dtprofit = profit
dtoptimal = threshold
We compute the variable importance.
dt_imp <- varImp(fit.c50, scale = F)
plot(dt_imp, scales = list(y = list(cex = .95)))
We see clearly that the most important variables are
redshift (as always), i and u,
with the same importance all of them.
partial(fit.c50, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(fit.c50, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(fit.c50, pred.var = "u", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
From the redshift graph we can draw the same conclusions
as before, but seeing that the probability does not increase to 1, but
only to 0.9. The variable i shows that with values less
than 20 they are not likely to be QSO and with higher than 23 they are
QSO. Between 20 and 23 the higher the more probable it is QSO, with a
decrease in probability in the middle. The variable u works
as the contrary, the higher the value, the lower the probability of
being classified as QSO.
Random Forest is a popular ensemble learning technique used for both classification and regression tasks. It operates by building multiple decision trees during the training phase and then combining their predictions to improve accuracy and reduce overfitting.
However, Random Forest may not be as interpretable as individual decision trees, especially when dealing with a large number of trees. Additionally, training a Random Forest model can be computationally expensive, especially with a large number of trees and features. Despite these drawbacks, Random Forest remains one of the most popular and widely used machine learning algorithms due to its excellent performance across various tasks and datasets.
Once again, we use caret to train the model with out economic profit in mind.
rf.train <- train(class ~.,
method = "rf",
data = train_data,
preProcess = c("center", "scale"),
ntree = 200,
cutoff=c(0.7,0.3),
tuneGrid = expand.grid(mtry=c(6,7,8,9,10)),
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
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## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 8 on full training set
rf.train
## Random Forest
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3200, 3200, 3201, 3199, 3200
## Resampling results across tuning parameters:
##
## mtry EconomicProfit
## 6 0.2285901
## 7 0.2275645
## 8 0.2273895
## 9 0.2278392
## 10 0.2282391
##
## EconomicProfit was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 8.
best_hyperparameters <- rf.train$bestTune
best_row <- rf.train$results[rf.train$results$mtry == best_hyperparameters$mtry, ]
profit <- best_row$EconomicProfit
We see that the hyper-parameter with highest economic profit is mtry = 8, which we are later going to use, so we store it. We use now this trained model to obtain our predictions and obtain the economic profit to be able to compare.
rfhyp = best_hyperparameters
rfPred = predict(rf.train, newdata=test_data)
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2286964
This is the best model yet, with the highest economic profit 0.2286964.
We try to improve it even more changing the threshold (first manually and then obtaining the optimal one).
Sometimes, the threshold in the Bayes rule is more important than hyper-parameters in the ML tools:
threshold = 0.2
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2289548
It got worse with this one to 0.2289548, let’s compute the optimal one.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = best_hyperparameters
j <- 0
for (threshold in seq(0.05,0.5,0.05)){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
rf.train <- train(class ~.,
method = "rf",
data = train,
preProcess = c("center", "scale"),
ntree = 200,
cutoff=c(0.7,0.3),
tuneGrid = grid,
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
rfProb = predict(rf.train, test, type="prob")
rfPred = rep("NOT.QSO", nrow(test))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(svmPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
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## Aggregating results
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## Aggregating results
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## Aggregating results
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## Aggregating results
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## Aggregating results
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## Aggregating results
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## Aggregating results
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq(0.05,0.5,0.05),col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.005356398 0.007357232 0.006023343 0.004689454 0.004689454 0.009358066
## [7] 0.008024177 0.004022509 0.004022509 0.004689454
We make our final predictions of the random forest model with the optimal threshold which we compute.
indexthr = which.max(medians)
threshold = seq(0.05,0.5,0.05)[indexthr]
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2245791
We obtain an economic profit of 0.2245791, that is slightly worse than the decision trees one. Our best model yet is with decision trees. We store its profit and optimal threshold.
rfprofit = profit
rfoptimal = threshold
Let`s see which are the most explanatory variables.
rf_imp <- varImp(rf.train, scale = F)
plot(rf_imp, scales = list(y = list(cex = .95)))
partial(rf.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We draw the same conclusion as always, but like in decision trees, the probability only increases until 0.9.
Gradient Boosting is a powerful machine learning technique used for both regression and classification tasks. It works by building a series of weak learners (typically decision trees) in a sequential manner, where each new learner corrects the errors made by the previous ones. Gradient Boosting combines the predictions of multiple weak learners to create a strong ensemble model.
Gradient Boosting has high predictive accuracy , it handles mixed data types (a mixture of numerical and categorical features), it automatically handles missing values (learns from data with missing values without imputation) and provides feature importance.
However, Gradient Boosting can be sensitive to hyperparameters and may require careful tuning to prevent overfitting. It can also be computationally expensive and slower to train compared to other algorithms. Despite these challenges, Gradient Boosting remains a popular and effective choice for various machine learning tasks.
We are going to use the caret package to train a model that uses gradient boosting and takes into account the economic nature of our task. Firstly, we set the very wide grid for hyper-parameter tuning and then, we train introducing this grid.
xgb_grid = expand.grid(
nrounds = c(500, 600, 700, 800, 1000),
eta = c(0.01, 0.001),
max_depth = c(2, 3, 4, 5, 6),
gamma = 1,
colsample_bytree = c(0.2, 0.25, 0.3, 0.35, 0.4),
min_child_weight = c(1, 2, 3, 4, 5),
subsample = 1
)
Then, train.
xgb.train = train(class ~ .,
data=train_data,
trControl = ctrl,
metric="EconomicProfit",
maximize = F,
tuneGrid = xgb_grid,
preProcess = c("center", "scale"),
method = "xgbTree"
)
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
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## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:39:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:50:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:03:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:13:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:17:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:30:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:56:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:56:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000
## Aggregating results
## Selecting tuning parameters
## Fitting nrounds = 500, max_depth = 2, eta = 0.001, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5, subsample = 1 on full training set
best_hyperparameters = xgb.train$bestTune
xgb.train
## eXtreme Gradient Boosting
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3199, 3200, 3201, 3200, 3200
## Resampling results across tuning parameters:
##
## eta max_depth colsample_bytree min_child_weight nrounds EconomicProfit
## 0.001 2 0.20 1 500 0.09554571
## 0.001 2 0.20 1 600 0.10304728
## 0.001 2 0.20 1 700 0.10858751
## 0.001 2 0.20 1 800 0.11466408
## 0.001 2 0.20 1 1000 0.12497620
## 0.001 2 0.20 2 500 0.09336173
## 0.001 2 0.20 2 600 0.09742345
## 0.001 2 0.20 2 700 0.10680080
## 0.001 2 0.20 2 800 0.11202463
## 0.001 2 0.20 2 1000 0.12006331
## 0.001 2 0.20 3 500 0.09734181
## 0.001 2 0.20 3 600 0.10336876
## 0.001 2 0.20 3 700 0.10859377
## 0.001 2 0.20 3 800 0.11234377
## 0.001 2 0.20 3 1000 0.12122854
## 0.001 2 0.20 4 500 0.10077462
## 0.001 2 0.20 4 600 0.10412579
## 0.001 2 0.20 4 700 0.10809962
## 0.001 2 0.20 4 800 0.11685080
## 0.001 2 0.20 4 1000 0.12520120
## 0.001 2 0.20 5 500 0.09001329
## 0.001 2 0.20 5 600 0.09836290
## 0.001 2 0.20 5 700 0.10327696
## 0.001 2 0.20 5 800 0.10859025
## 0.001 2 0.20 5 1000 0.11881487
## 0.001 2 0.25 1 500 0.12738491
## 0.001 2 0.25 1 600 0.13082436
## 0.001 2 0.25 1 700 0.13479975
## 0.001 2 0.25 1 800 0.13823686
## 0.001 2 0.25 1 1000 0.14167436
## 0.001 2 0.25 2 500 0.12841343
## 0.001 2 0.25 2 600 0.13278843
## 0.001 2 0.25 2 700 0.13426187
## 0.001 2 0.25 2 800 0.13605132
## 0.001 2 0.25 2 1000 0.13917554
## 0.001 2 0.25 3 500 0.12256995
## 0.001 2 0.25 3 600 0.12551877
## 0.001 2 0.25 3 700 0.13105444
## 0.001 2 0.25 3 800 0.13636577
## 0.001 2 0.25 3 1000 0.14042593
## 0.001 2 0.25 4 500 0.12747553
## 0.001 2 0.25 4 600 0.13332436
## 0.001 2 0.25 4 700 0.13707358
## 0.001 2 0.25 4 800 0.13957241
## 0.001 2 0.25 4 1000 0.14229819
## 0.001 2 0.25 5 500 0.12488724
## 0.001 2 0.25 5 600 0.13051264
## 0.001 2 0.25 5 700 0.13301264
## 0.001 2 0.25 5 800 0.13667397
## 0.001 2 0.25 5 1000 0.14229858
## 0.001 2 0.30 1 500 0.14417476
## 0.001 2 0.30 1 600 0.14605015
## 0.001 2 0.30 1 700 0.14730015
## 0.001 2 0.30 1 800 0.15042515
## 0.001 2 0.30 1 1000 0.15315015
## 0.001 2 0.30 2 500 0.14542749
## 0.001 2 0.30 2 600 0.14783999
## 0.001 2 0.30 2 700 0.14698882
## 0.001 2 0.30 2 800 0.15073726
## 0.001 2 0.30 2 1000 0.15354976
## 0.001 2 0.30 3 500 0.14752476
## 0.001 2 0.30 3 600 0.14940015
## 0.001 2 0.30 3 700 0.14940015
## 0.001 2 0.30 3 800 0.15189976
## 0.001 2 0.30 3 1000 0.15292476
## 0.001 2 0.30 4 500 0.14533687
## 0.001 2 0.30 4 600 0.14783687
## 0.001 2 0.30 4 700 0.14783648
## 0.001 2 0.30 4 800 0.15096148
## 0.001 2 0.30 4 1000 0.15408726
## 0.001 2 0.30 5 500 0.13948959
## 0.001 2 0.30 5 600 0.14355093
## 0.001 2 0.30 5 700 0.14730015
## 0.001 2 0.30 5 800 0.14948804
## 0.001 2 0.30 5 1000 0.15252476
## 0.001 2 0.35 1 500 0.15775133
## 0.001 2 0.35 1 600 0.15900055
## 0.001 2 0.35 1 700 0.16047555
## 0.001 2 0.35 1 800 0.16266071
## 0.001 2 0.35 1 1000 0.17069821
## 0.001 2 0.35 2 500 0.15618844
## 0.001 2 0.35 2 600 0.15618765
## 0.001 2 0.35 2 700 0.15859937
## 0.001 2 0.35 2 800 0.16172399
## 0.001 2 0.35 2 1000 0.16609587
## 0.001 2 0.35 3 500 0.15587633
## 0.001 2 0.35 3 600 0.15618804
## 0.001 2 0.35 3 700 0.15703883
## 0.001 2 0.35 3 800 0.15922633
## 0.001 2 0.35 3 1000 0.16360016
## 0.001 2 0.35 4 500 0.15618961
## 0.001 2 0.35 4 600 0.15775289
## 0.001 2 0.35 4 700 0.15953922
## 0.001 2 0.35 4 800 0.16110055
## 0.001 2 0.35 4 1000 0.16476110
## 0.001 2 0.35 5 500 0.15556187
## 0.001 2 0.35 5 600 0.15775054
## 0.001 2 0.35 5 700 0.15891187
## 0.001 2 0.35 5 800 0.16109781
## 0.001 2 0.35 5 1000 0.16632047
## 0.001 2 0.40 1 500 0.16555953
## 0.001 2 0.40 1 600 0.16609860
## 0.001 2 0.40 1 700 0.17515564
## 0.001 2 0.40 1 800 0.17890525
## 0.001 2 0.40 1 1000 0.18350759
## 0.001 2 0.40 2 500 0.16672242
## 0.001 2 0.40 2 600 0.17412946
## 0.001 2 0.40 2 700 0.17529207
## 0.001 2 0.40 2 800 0.17497957
## 0.001 2 0.40 2 1000 0.18060642
## 0.001 2 0.40 3 500 0.16557087
## 0.001 2 0.40 3 600 0.16985719
## 0.001 2 0.40 3 700 0.17579196
## 0.001 2 0.40 3 800 0.18172791
## 0.001 2 0.40 3 1000 0.18623689
## 0.001 2 0.40 4 500 0.16149625
## 0.001 2 0.40 4 600 0.16390993
## 0.001 2 0.40 4 700 0.16734665
## 0.001 2 0.40 4 800 0.17203884
## 0.001 2 0.40 4 1000 0.18154979
## 0.001 2 0.40 5 500 0.16181422
## 0.001 2 0.40 5 600 0.16766891
## 0.001 2 0.40 5 700 0.16891735
## 0.001 2 0.40 5 800 0.17578923
## 0.001 2 0.40 5 1000 0.18561189
## 0.001 3 0.20 1 500 0.12242698
## 0.001 3 0.20 1 600 0.12742776
## 0.001 3 0.20 1 700 0.12961565
## 0.001 3 0.20 1 800 0.13336566
## 0.001 3 0.20 1 1000 0.13805238
## 0.001 3 0.20 2 500 0.12898909
## 0.001 3 0.20 2 600 0.13242581
## 0.001 3 0.20 2 700 0.13367582
## 0.001 3 0.20 2 800 0.13523832
## 0.001 3 0.20 2 1000 0.14046332
## 0.001 3 0.20 3 500 0.12064104
## 0.001 3 0.20 3 600 0.12649143
## 0.001 3 0.20 3 700 0.12930315
## 0.001 3 0.20 3 800 0.13305276
## 0.001 3 0.20 3 1000 0.13742777
## 0.001 3 0.20 4 500 0.12023674
## 0.001 3 0.20 4 600 0.12586370
## 0.001 3 0.20 4 700 0.12961331
## 0.001 3 0.20 4 800 0.13305120
## 0.001 3 0.20 4 1000 0.13680160
## 0.001 3 0.20 5 500 0.10845197
## 0.001 3 0.20 5 600 0.11867932
## 0.001 3 0.20 5 700 0.12149260
## 0.001 3 0.20 5 800 0.12524104
## 0.001 3 0.20 5 1000 0.13180159
## 0.001 3 0.25 1 500 0.14474965
## 0.001 3 0.25 1 600 0.14528715
## 0.001 3 0.25 1 700 0.14653637
## 0.001 3 0.25 1 800 0.15028520
## 0.001 3 0.25 1 1000 0.15747231
## 0.001 3 0.25 2 500 0.14193910
## 0.001 3 0.25 2 600 0.14443832
## 0.001 3 0.25 2 700 0.14568793
## 0.001 3 0.25 2 800 0.14662543
## 0.001 3 0.25 2 1000 0.15395122
## 0.001 3 0.25 3 500 0.14506293
## 0.001 3 0.25 3 600 0.14693793
## 0.001 3 0.25 3 700 0.14818832
## 0.001 3 0.25 3 800 0.15037465
## 0.001 3 0.25 3 1000 0.15872466
## 0.001 3 0.25 4 500 0.14242465
## 0.001 3 0.25 4 600 0.14367504
## 0.001 3 0.25 4 700 0.14773793
## 0.001 3 0.25 4 800 0.14890082
## 0.001 3 0.25 4 1000 0.15497778
## 0.001 3 0.25 5 500 0.14086215
## 0.001 3 0.25 5 600 0.14546137
## 0.001 3 0.25 5 700 0.14639926
## 0.001 3 0.25 5 800 0.14889848
## 0.001 3 0.25 5 1000 0.15318637
## 0.001 3 0.30 1 500 0.15364184
## 0.001 3 0.30 1 600 0.15792973
## 0.001 3 0.30 1 700 0.16042934
## 0.001 3 0.30 1 800 0.16417583
## 0.001 3 0.30 1 1000 0.17136177
## 0.001 3 0.30 2 500 0.15667632
## 0.001 3 0.30 2 600 0.16127594
## 0.001 3 0.30 2 700 0.16939782
## 0.001 3 0.30 2 800 0.17096149
## 0.001 3 0.30 2 1000 0.17962400
## 0.001 3 0.30 3 500 0.16207310
## 0.001 3 0.30 3 600 0.16863482
## 0.001 3 0.30 3 700 0.17167087
## 0.001 3 0.30 3 800 0.17385954
## 0.001 3 0.30 3 1000 0.17721072
## 0.001 3 0.30 4 500 0.15247660
## 0.001 3 0.30 4 600 0.15488832
## 0.001 3 0.30 4 700 0.15957505
## 0.001 3 0.30 4 800 0.16238950
## 0.001 3 0.30 4 1000 0.17136060
## 0.001 3 0.30 5 500 0.16010602
## 0.001 3 0.30 5 600 0.16260680
## 0.001 3 0.30 5 700 0.16783376
## 0.001 3 0.30 5 800 0.16948415
## 0.001 3 0.30 5 1000 0.17823337
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## 0.010 6 0.20 3 500 0.20549003
## 0.010 6 0.20 3 600 0.20852714
## 0.010 6 0.20 3 700 0.21173993
## 0.010 6 0.20 3 800 0.21415293
## 0.010 6 0.20 3 1000 0.21696532
## 0.010 6 0.20 4 500 0.20593964
## 0.010 6 0.20 4 600 0.20946425
## 0.010 6 0.20 4 700 0.21165254
## 0.010 6 0.20 4 800 0.21491532
## 0.010 6 0.20 4 1000 0.21687782
## 0.010 6 0.20 5 500 0.20486436
## 0.010 6 0.20 5 600 0.20915069
## 0.010 6 0.20 5 700 0.21048886
## 0.010 6 0.20 5 800 0.21415215
## 0.010 6 0.20 5 1000 0.21634043
## 0.010 6 0.25 1 500 0.21106476
## 0.010 6 0.25 1 600 0.21401582
## 0.010 6 0.25 1 700 0.21682832
## 0.010 6 0.25 1 800 0.21754043
## 0.010 6 0.25 1 1000 0.21870332
## 0.010 6 0.25 2 500 0.21182636
## 0.010 6 0.25 2 600 0.21321376
## 0.010 6 0.25 2 700 0.21625204
## 0.010 6 0.25 2 800 0.21687704
## 0.010 6 0.25 2 1000 0.21977821
## 0.010 6 0.25 3 500 0.21245175
## 0.010 6 0.25 3 600 0.21602665
## 0.010 6 0.25 3 700 0.21682743
## 0.010 6 0.25 3 800 0.21776532
## 0.010 6 0.25 3 1000 0.22071571
## 0.010 6 0.25 4 500 0.21298993
## 0.010 6 0.25 4 600 0.21495204
## 0.010 6 0.25 4 700 0.21620282
## 0.010 6 0.25 4 800 0.21714121
## 0.010 6 0.25 4 1000 0.21889149
## 0.010 6 0.25 5 500 0.20941464
## 0.010 6 0.25 5 600 0.21446571
## 0.010 6 0.25 5 700 0.21647782
## 0.010 6 0.25 5 800 0.21781571
## 0.010 6 0.25 5 1000 0.22036571
## 0.010 6 0.30 1 500 0.21503993
## 0.010 6 0.30 1 600 0.21588993
## 0.010 6 0.30 1 700 0.21776571
## 0.010 6 0.30 1 800 0.21892899
## 0.010 6 0.30 1 1000 0.22134138
## 0.010 6 0.30 2 500 0.21557665
## 0.010 6 0.30 2 600 0.21651454
## 0.010 6 0.30 2 700 0.21844032
## 0.010 6 0.30 2 800 0.21969071
## 0.010 6 0.30 2 1000 0.22054110
## 0.010 6 0.30 3 500 0.21464082
## 0.010 6 0.30 3 600 0.21714121
## 0.010 6 0.30 3 700 0.21861660
## 0.010 6 0.30 3 800 0.21986621
## 0.010 6 0.30 3 1000 0.21991632
## 0.010 6 0.30 4 500 0.21495282
## 0.010 6 0.30 4 600 0.21526532
## 0.010 6 0.30 4 700 0.21714110
## 0.010 6 0.30 4 800 0.21826649
## 0.010 6 0.30 4 1000 0.22147860
## 0.010 6 0.30 5 500 0.21383993
## 0.010 6 0.30 5 600 0.21540321
## 0.010 6 0.30 5 700 0.21804149
## 0.010 6 0.30 5 800 0.21812849
## 0.010 6 0.30 5 1000 0.22125321
## 0.010 6 0.35 1 500 0.21807693
## 0.010 6 0.35 1 600 0.21995310
## 0.010 6 0.35 1 700 0.22182889
## 0.010 6 0.35 1 800 0.22384061
## 0.010 6 0.35 1 1000 0.22429033
## 0.010 6 0.35 2 500 0.21830076
## 0.010 6 0.35 2 600 0.22227821
## 0.010 6 0.35 2 700 0.22330282
## 0.010 6 0.35 2 800 0.22375333
## 0.010 6 0.35 2 1000 0.22415322
## 0.010 6 0.35 3 500 0.21812821
## 0.010 6 0.35 3 600 0.22022849
## 0.010 6 0.35 3 700 0.22227761
## 0.010 6 0.35 3 800 0.22210310
## 0.010 6 0.35 3 1000 0.22429100
## 0.010 6 0.35 4 500 0.21986610
## 0.010 6 0.35 4 600 0.22049099
## 0.010 6 0.35 4 700 0.22134021
## 0.010 6 0.35 4 800 0.22241482
## 0.010 6 0.35 4 1000 0.22375233
## 0.010 6 0.35 5 500 0.21901571
## 0.010 6 0.35 5 600 0.21969099
## 0.010 6 0.35 5 700 0.22116610
## 0.010 6 0.35 5 800 0.22241571
## 0.010 6 0.35 5 1000 0.22429072
## 0.010 6 0.40 1 500 0.22106532
## 0.010 6 0.40 1 600 0.22222771
## 0.010 6 0.40 1 700 0.22375282
## 0.010 6 0.40 1 800 0.22375282
## 0.010 6 0.40 1 1000 0.22406532
## 0.010 6 0.40 2 500 0.22182771
## 0.010 6 0.40 2 600 0.22290300
## 0.010 6 0.40 2 700 0.22352721
## 0.010 6 0.40 2 800 0.22303982
## 0.010 6 0.40 2 1000 0.22406522
## 0.010 6 0.40 3 500 0.21995410
## 0.010 6 0.40 3 600 0.22116610
## 0.010 6 0.40 3 700 0.22201610
## 0.010 6 0.40 3 800 0.22232821
## 0.010 6 0.40 3 1000 0.22340243
## 0.010 6 0.40 4 500 0.22040399
## 0.010 6 0.40 4 600 0.22116699
## 0.010 6 0.40 4 700 0.22184110
## 0.010 6 0.40 4 800 0.22255321
## 0.010 6 0.40 4 1000 0.22309032
## 0.010 6 0.40 5 500 0.21977782
## 0.010 6 0.40 5 600 0.22156560
## 0.010 6 0.40 5 700 0.22227771
## 0.010 6 0.40 5 800 0.22375272
## 0.010 6 0.40 5 1000 0.22460154
##
## Tuning parameter 'gamma' was held constant at a value of 1
## Tuning
## parameter 'subsample' was held constant at a value of 1
## EconomicProfit was used to select the optimal model using the smallest value.
## The final values used for the model were nrounds = 500, max_depth = 2, eta
## = 0.001, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5 and
## subsample = 1.
We see that the best hyper-parameters are: nrounds = 500, eta = 0.001, max_depth = 2, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5 and subsample = 1.
With it, we obtain the predictions and see how well it performs according to our economic profit metric. We now use this model to predict directly using the probability to be able to change the threshold.
threshold = 0.4
xgbProb = predict(xgb.train, newdata=test_data, type="prob")
xgbPred = rep("NOT.QSO", nrow(test_data))
xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(xgbPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.1983331
Worse than our previous model, let’s try to improve it looking for the optimal threshold.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = best_hyperparameters
j <- 0
for (threshold in seq(0.25, 0.7, 0.05)){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
xgb.train = train(class ~ .,
data=train,
trControl = ctrl,
metric="EconomicProfit",
maximize = F,
tuneGrid = grid,
preProcess = c("center", "scale"),
method = "xgbTree"
)
xgbProb = predict(xgb.train, test, type="prob")
xgbPred = rep("NOT.QSO", nrow(test))
xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(xgbPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1
## Aggregating results
## Fitting final model on full training set
## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq(0.05, 0.5, 0.05), col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] -0.03288870 -0.03288870 0.12817841 0.19877032 0.16936223 0.09845769
## [7] 0.01031680 0.01031680 0.01031680 0.01031680
Final prediction with the obtained hyper-parameters and optimal threshold.
indexthr = which.max(medians)
threshold = seq(0.25, 0.7, 0.05)[indexthr]
xgbProb = predict(xgb.train, newdata=test_data, type="prob")
xgbPred = rep("NOT.QSO", nrow(test_data))
xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(xgbPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2004501
We still do not have a model better than decision trees, we keep trying with neural networks later.
We study to see which are the most informative variables.
gb_imp <- varImp(xgb.train, scale = F)
plot(gb_imp, scales = list(y = list(cex = .95)))
redshift is once again the most influential
variable.
partial(xgb.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We obtain the same conclusion as always, but in this one we see that the increase in probability is much more drastic and only until 0.44.
Neural networks are a class of machine learning models inspired by the structure and functioning of the human brain’s biological neural networks. They consist of interconnected nodes, called neurons or units, organized in layers. Neural networks are capable of learning complex relationships and patterns directly from data, making them powerful tools for various machine learning tasks, including classification, regression, and pattern recognition.
Neural networks can vary significantly in architecture, including the number of layers, the number of neurons in each layer, the type of activation functions used, and the specific training algorithm employed. Neural networks have gained popularity in recent years due to their ability to learn from large, complex datasets and achieve state-of-the-art performance in various domains, including computer vision, natural language processing, and speech recognition. However, training neural networks can be computationally intensive and requires large amounts of data
We use caret to train our model with neural networks and, then, we plot it to observe it.
nn.train <- train(class ~.,
method = "nnet",
data = train_data,
preProcess = c("center", "scale"),
MaxNWts = 1000,
maxit = 100,
tuneGrid = expand.grid(size=c(2,3,4,5,6), decay=c(0.01,0.007,0.005,0.003,0.001)),
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
## # weights: 57
## initial value 2749.423471
## iter 10 value 896.500476
## iter 20 value 621.809845
## iter 30 value 460.500338
## iter 40 value 376.771069
## iter 50 value 340.760561
## iter 60 value 332.565278
## iter 70 value 319.374754
## iter 80 value 295.871616
## iter 90 value 288.754572
## iter 100 value 285.723729
## final value 285.723729
## stopped after 100 iterations
## # weights: 85
## initial value 1657.718658
## iter 10 value 606.999038
## iter 20 value 420.975656
## iter 30 value 340.768452
## iter 40 value 276.370944
## iter 50 value 259.167287
## iter 60 value 252.715352
## iter 70 value 247.296982
## iter 80 value 240.893010
## iter 90 value 237.191317
## iter 100 value 236.122303
## final value 236.122303
## stopped after 100 iterations
## # weights: 113
## initial value 1726.709954
## iter 10 value 364.291290
## iter 20 value 288.622458
## iter 30 value 250.936551
## iter 40 value 220.509779
## iter 50 value 208.054272
## iter 60 value 199.036729
## iter 70 value 194.708816
## iter 80 value 190.902671
## iter 90 value 188.883730
## iter 100 value 186.458136
## final value 186.458136
## stopped after 100 iterations
## # weights: 141
## initial value 2057.773611
## iter 10 value 341.844567
## iter 20 value 257.942092
## iter 30 value 224.734260
## iter 40 value 206.519976
## iter 50 value 195.053823
## iter 60 value 187.326897
## iter 70 value 184.027639
## iter 80 value 182.060038
## iter 90 value 180.552301
## iter 100 value 179.496962
## final value 179.496962
## stopped after 100 iterations
## # weights: 169
## initial value 1813.761675
## iter 10 value 317.969267
## iter 20 value 237.407530
## iter 30 value 204.863068
## iter 40 value 188.018121
## iter 50 value 175.460233
## iter 60 value 168.473984
## iter 70 value 160.744201
## iter 80 value 154.753231
## iter 90 value 151.251156
## iter 100 value 147.890613
## final value 147.890613
## stopped after 100 iterations
## # weights: 57
## initial value 1710.440174
## iter 10 value 724.170127
## iter 20 value 596.912927
## iter 30 value 457.563238
## iter 40 value 342.254923
## iter 50 value 313.027575
## iter 60 value 305.144645
## iter 70 value 303.002611
## iter 80 value 300.692066
## iter 90 value 299.641902
## iter 100 value 298.740044
## final value 298.740044
## stopped after 100 iterations
## # weights: 85
## initial value 2800.030247
## iter 10 value 489.683683
## iter 20 value 382.124940
## iter 30 value 311.107567
## iter 40 value 269.826796
## iter 50 value 246.238953
## iter 60 value 227.252232
## iter 70 value 217.032756
## iter 80 value 213.809221
## iter 90 value 213.084229
## iter 100 value 212.102709
## final value 212.102709
## stopped after 100 iterations
## # weights: 113
## initial value 2215.437611
## iter 10 value 561.442400
## iter 20 value 313.957617
## iter 30 value 256.591168
## iter 40 value 239.253178
## iter 50 value 225.716292
## iter 60 value 210.275286
## iter 70 value 203.167648
## iter 80 value 201.900003
## iter 90 value 198.173371
## iter 100 value 196.308885
## final value 196.308885
## stopped after 100 iterations
## # weights: 141
## initial value 1960.870351
## iter 10 value 337.841749
## iter 20 value 275.829431
## iter 30 value 252.217945
## iter 40 value 240.734251
## iter 50 value 230.776686
## iter 60 value 211.550098
## iter 70 value 197.414616
## iter 80 value 190.845331
## iter 90 value 186.835130
## iter 100 value 184.355449
## final value 184.355449
## stopped after 100 iterations
## # weights: 169
## initial value 2180.196107
## iter 10 value 395.993661
## iter 20 value 278.319696
## iter 30 value 231.324681
## iter 40 value 204.487969
## iter 50 value 185.412465
## iter 60 value 170.036618
## iter 70 value 163.627578
## iter 80 value 160.244521
## iter 90 value 158.154921
## iter 100 value 156.586922
## final value 156.586922
## stopped after 100 iterations
## # weights: 57
## initial value 2002.885995
## iter 10 value 945.025779
## iter 20 value 612.776297
## iter 30 value 447.678878
## iter 40 value 347.899997
## iter 50 value 320.536070
## iter 60 value 312.824784
## iter 70 value 311.490504
## iter 80 value 310.436779
## iter 90 value 309.422219
## iter 100 value 309.205439
## final value 309.205439
## stopped after 100 iterations
## # weights: 85
## initial value 3036.946498
## iter 10 value 580.685384
## iter 20 value 371.819134
## iter 30 value 314.921658
## iter 40 value 288.114417
## iter 50 value 275.929721
## iter 60 value 267.107404
## iter 70 value 256.688779
## iter 80 value 250.651985
## iter 90 value 249.558313
## iter 100 value 247.971640
## final value 247.971640
## stopped after 100 iterations
## # weights: 113
## initial value 2540.384249
## iter 10 value 489.629070
## iter 20 value 316.004942
## iter 30 value 269.103009
## iter 40 value 244.163468
## iter 50 value 236.827263
## iter 60 value 232.944000
## iter 70 value 226.523193
## iter 80 value 218.298014
## iter 90 value 215.407056
## iter 100 value 211.731867
## final value 211.731867
## stopped after 100 iterations
## # weights: 141
## initial value 2752.715940
## iter 10 value 299.620566
## iter 20 value 232.191081
## iter 30 value 209.601833
## iter 40 value 192.709325
## iter 50 value 174.184845
## iter 60 value 158.515628
## iter 70 value 147.525278
## iter 80 value 143.643650
## iter 90 value 142.119988
## iter 100 value 141.364596
## final value 141.364596
## stopped after 100 iterations
## # weights: 169
## initial value 2427.304996
## iter 10 value 329.546381
## iter 20 value 246.390581
## iter 30 value 208.139938
## iter 40 value 189.940982
## iter 50 value 174.633143
## iter 60 value 156.400700
## iter 70 value 148.786128
## iter 80 value 144.265258
## iter 90 value 142.604781
## iter 100 value 139.948524
## final value 139.948524
## stopped after 100 iterations
## # weights: 57
## initial value 2265.703546
## iter 10 value 556.856981
## iter 20 value 491.380549
## iter 30 value 376.021430
## iter 40 value 319.782210
## iter 50 value 293.748318
## iter 60 value 285.890820
## iter 70 value 283.502791
## iter 80 value 274.036601
## iter 90 value 256.422462
## iter 100 value 252.111870
## final value 252.111870
## stopped after 100 iterations
## # weights: 85
## initial value 1727.882522
## iter 10 value 323.947786
## iter 20 value 284.509085
## iter 30 value 252.947371
## iter 40 value 237.079567
## iter 50 value 220.065538
## iter 60 value 210.658716
## iter 70 value 205.046243
## iter 80 value 201.952297
## iter 90 value 199.013044
## iter 100 value 197.498646
## final value 197.498646
## stopped after 100 iterations
## # weights: 113
## initial value 2335.408174
## iter 10 value 550.488904
## iter 20 value 298.506833
## iter 30 value 246.069742
## iter 40 value 212.220915
## iter 50 value 184.781007
## iter 60 value 169.595187
## iter 70 value 162.278948
## iter 80 value 155.819333
## iter 90 value 153.149616
## iter 100 value 152.525685
## final value 152.525685
## stopped after 100 iterations
## # weights: 141
## initial value 2122.590447
## iter 10 value 437.636292
## iter 20 value 327.595191
## iter 30 value 279.356386
## iter 40 value 245.470173
## iter 50 value 222.879199
## iter 60 value 207.819005
## iter 70 value 185.490396
## iter 80 value 177.669339
## iter 90 value 174.577463
## iter 100 value 169.506253
## final value 169.506253
## stopped after 100 iterations
## # weights: 169
## initial value 1817.996674
## iter 10 value 341.812584
## iter 20 value 251.027658
## iter 30 value 202.212531
## iter 40 value 182.652763
## iter 50 value 162.763308
## iter 60 value 153.966620
## iter 70 value 148.861927
## iter 80 value 145.691264
## iter 90 value 143.480236
## iter 100 value 142.447418
## final value 142.447418
## stopped after 100 iterations
## # weights: 57
## initial value 2368.471471
## iter 10 value 1049.305059
## iter 20 value 744.130623
## iter 30 value 674.156838
## iter 40 value 337.088211
## iter 50 value 296.470536
## iter 60 value 266.912615
## iter 70 value 260.085831
## iter 80 value 258.001851
## iter 90 value 256.996377
## iter 100 value 256.040976
## final value 256.040976
## stopped after 100 iterations
## # weights: 85
## initial value 2559.602369
## iter 10 value 396.880282
## iter 20 value 276.083814
## iter 30 value 258.057178
## iter 40 value 246.318450
## iter 50 value 237.409149
## iter 60 value 231.542543
## iter 70 value 229.243851
## iter 80 value 226.578152
## iter 90 value 222.616649
## iter 100 value 219.450595
## final value 219.450595
## stopped after 100 iterations
## # weights: 113
## initial value 3000.012904
## iter 10 value 456.300219
## iter 20 value 291.104395
## iter 30 value 233.585041
## iter 40 value 211.788407
## iter 50 value 196.534567
## iter 60 value 188.632123
## iter 70 value 183.202862
## iter 80 value 181.570088
## iter 90 value 180.580407
## iter 100 value 179.365913
## final value 179.365913
## stopped after 100 iterations
## # weights: 141
## initial value 2362.435857
## iter 10 value 337.358817
## iter 20 value 257.517387
## iter 30 value 234.038193
## iter 40 value 210.022646
## iter 50 value 190.087776
## iter 60 value 180.273626
## iter 70 value 174.961544
## iter 80 value 170.925675
## iter 90 value 167.638299
## iter 100 value 164.255602
## final value 164.255602
## stopped after 100 iterations
## # weights: 169
## initial value 1948.504967
## iter 10 value 321.538761
## iter 20 value 239.638510
## iter 30 value 192.462920
## iter 40 value 168.461577
## iter 50 value 153.282895
## iter 60 value 141.438355
## iter 70 value 135.425383
## iter 80 value 132.417379
## iter 90 value 130.256058
## iter 100 value 126.510803
## final value 126.510803
## stopped after 100 iterations
## # weights: 57
## initial value 3575.586116
## iter 10 value 579.770882
## iter 20 value 472.695488
## iter 30 value 424.139641
## iter 40 value 360.499340
## iter 50 value 317.715749
## iter 60 value 294.505148
## iter 70 value 287.237688
## iter 80 value 282.519530
## iter 90 value 276.653421
## iter 100 value 273.239731
## final value 273.239731
## stopped after 100 iterations
## # weights: 85
## initial value 2708.975966
## iter 10 value 826.697916
## iter 20 value 390.346886
## iter 30 value 328.987241
## iter 40 value 298.625437
## iter 50 value 283.087027
## iter 60 value 278.263376
## iter 70 value 274.382035
## iter 80 value 268.794655
## iter 90 value 265.127609
## iter 100 value 263.493974
## final value 263.493974
## stopped after 100 iterations
## # weights: 113
## initial value 1873.704695
## iter 10 value 313.237068
## iter 20 value 254.732695
## iter 30 value 234.709063
## iter 40 value 228.405119
## iter 50 value 223.994302
## iter 60 value 216.740513
## iter 70 value 209.582189
## iter 80 value 205.593702
## iter 90 value 204.634128
## iter 100 value 204.138262
## final value 204.138262
## stopped after 100 iterations
## # weights: 141
## initial value 1808.925943
## iter 10 value 529.906525
## iter 20 value 387.650105
## iter 30 value 300.281195
## iter 40 value 256.687688
## iter 50 value 235.814282
## iter 60 value 229.112556
## iter 70 value 225.855049
## iter 80 value 224.067308
## iter 90 value 220.992743
## iter 100 value 218.639974
## final value 218.639974
## stopped after 100 iterations
## # weights: 169
## initial value 1625.083003
## iter 10 value 297.717109
## iter 20 value 234.679776
## iter 30 value 219.730954
## iter 40 value 191.912019
## iter 50 value 181.889794
## iter 60 value 172.630750
## iter 70 value 166.495658
## iter 80 value 162.588577
## iter 90 value 158.149327
## iter 100 value 152.403933
## final value 152.403933
## stopped after 100 iterations
## # weights: 57
## initial value 1796.918674
## iter 10 value 641.763027
## iter 20 value 475.234711
## iter 30 value 402.939709
## iter 40 value 356.223682
## iter 50 value 332.960564
## iter 60 value 325.247664
## iter 70 value 322.258503
## iter 80 value 320.197022
## iter 90 value 316.598908
## iter 100 value 304.422162
## final value 304.422162
## stopped after 100 iterations
## # weights: 85
## initial value 2141.753735
## iter 10 value 478.465547
## iter 20 value 308.999910
## iter 30 value 259.630422
## iter 40 value 236.207333
## iter 50 value 225.943737
## iter 60 value 223.600723
## iter 70 value 221.065278
## iter 80 value 218.564814
## iter 90 value 217.717843
## iter 100 value 216.844565
## final value 216.844565
## stopped after 100 iterations
## # weights: 113
## initial value 1836.254296
## iter 10 value 407.536186
## iter 20 value 283.362563
## iter 30 value 246.758410
## iter 40 value 229.344952
## iter 50 value 211.664817
## iter 60 value 198.259520
## iter 70 value 191.286083
## iter 80 value 187.366488
## iter 90 value 184.074834
## iter 100 value 182.204458
## final value 182.204458
## stopped after 100 iterations
## # weights: 141
## initial value 1569.717755
## iter 10 value 292.816447
## iter 20 value 222.438587
## iter 30 value 193.218642
## iter 40 value 178.906036
## iter 50 value 172.536177
## iter 60 value 168.982540
## iter 70 value 165.897173
## iter 80 value 163.584936
## iter 90 value 159.116159
## iter 100 value 158.403864
## final value 158.403864
## stopped after 100 iterations
## # weights: 169
## initial value 4258.370124
## iter 10 value 366.318137
## iter 20 value 252.312408
## iter 30 value 198.403364
## iter 40 value 176.155218
## iter 50 value 155.370952
## iter 60 value 149.560087
## iter 70 value 145.376520
## iter 80 value 142.684972
## iter 90 value 139.982961
## iter 100 value 137.399162
## final value 137.399162
## stopped after 100 iterations
## # weights: 57
## initial value 2654.233658
## iter 10 value 670.729194
## iter 20 value 413.009856
## iter 30 value 331.087339
## iter 40 value 310.669754
## iter 50 value 299.190706
## iter 60 value 290.187230
## iter 70 value 277.934995
## iter 80 value 271.286961
## iter 90 value 268.844025
## iter 100 value 263.848330
## final value 263.848330
## stopped after 100 iterations
## # weights: 85
## initial value 1622.868031
## iter 10 value 668.733327
## iter 20 value 339.716898
## iter 30 value 290.420924
## iter 40 value 277.966696
## iter 50 value 267.260789
## iter 60 value 251.876012
## iter 70 value 239.582179
## iter 80 value 234.789629
## iter 90 value 232.324589
## iter 100 value 228.878877
## final value 228.878877
## stopped after 100 iterations
## # weights: 113
## initial value 3818.514041
## iter 10 value 689.714562
## iter 20 value 393.310623
## iter 30 value 322.307279
## iter 40 value 292.882322
## iter 50 value 282.101812
## iter 60 value 269.860857
## iter 70 value 258.509213
## iter 80 value 246.991703
## iter 90 value 232.030366
## iter 100 value 225.682456
## final value 225.682456
## stopped after 100 iterations
## # weights: 141
## initial value 2565.564801
## iter 10 value 384.509485
## iter 20 value 314.729537
## iter 30 value 275.236372
## iter 40 value 240.924683
## iter 50 value 208.369627
## iter 60 value 197.930658
## iter 70 value 189.802027
## iter 80 value 186.021860
## iter 90 value 184.430278
## iter 100 value 183.982880
## final value 183.982880
## stopped after 100 iterations
## # weights: 169
## initial value 2748.733184
## iter 10 value 285.443200
## iter 20 value 223.481437
## iter 30 value 181.822031
## iter 40 value 157.865191
## iter 50 value 140.497373
## iter 60 value 132.161538
## iter 70 value 124.515078
## iter 80 value 118.768564
## iter 90 value 114.575456
## iter 100 value 112.076493
## final value 112.076493
## stopped after 100 iterations
## # weights: 57
## initial value 1709.081775
## iter 10 value 351.268090
## iter 20 value 292.228368
## iter 30 value 278.242855
## iter 40 value 276.835020
## iter 50 value 276.444790
## iter 60 value 274.984818
## iter 70 value 272.614507
## iter 80 value 269.561282
## iter 90 value 268.855206
## iter 100 value 268.616944
## final value 268.616944
## stopped after 100 iterations
## # weights: 85
## initial value 2264.746480
## iter 10 value 290.754071
## iter 20 value 252.815581
## iter 30 value 233.315101
## iter 40 value 224.482557
## iter 50 value 222.274419
## iter 60 value 220.408363
## iter 70 value 219.057109
## iter 80 value 217.356016
## iter 90 value 215.153720
## iter 100 value 213.788713
## final value 213.788713
## stopped after 100 iterations
## # weights: 113
## initial value 1903.864893
## iter 10 value 410.640864
## iter 20 value 302.054010
## iter 30 value 242.028602
## iter 40 value 210.590118
## iter 50 value 202.563997
## iter 60 value 199.893818
## iter 70 value 198.219565
## iter 80 value 196.089247
## iter 90 value 193.809206
## iter 100 value 193.281926
## final value 193.281926
## stopped after 100 iterations
## # weights: 141
## initial value 2993.990322
## iter 10 value 301.006233
## iter 20 value 240.840705
## iter 30 value 193.020902
## iter 40 value 164.866523
## iter 50 value 146.101399
## iter 60 value 137.257507
## iter 70 value 131.728468
## iter 80 value 130.123017
## iter 90 value 129.052002
## iter 100 value 128.402206
## final value 128.402206
## stopped after 100 iterations
## # weights: 169
## initial value 3885.445981
## iter 10 value 408.511015
## iter 20 value 231.777055
## iter 30 value 196.973799
## iter 40 value 177.254578
## iter 50 value 165.130930
## iter 60 value 159.154273
## iter 70 value 155.343443
## iter 80 value 148.207659
## iter 90 value 143.870385
## iter 100 value 140.184873
## final value 140.184873
## stopped after 100 iterations
## # weights: 57
## initial value 2735.509831
## iter 10 value 667.385415
## iter 20 value 436.884734
## iter 30 value 318.790503
## iter 40 value 295.658685
## iter 50 value 278.729255
## iter 60 value 273.186482
## iter 70 value 270.229292
## iter 80 value 269.371630
## iter 90 value 268.775753
## iter 100 value 265.845791
## final value 265.845791
## stopped after 100 iterations
## # weights: 85
## initial value 4082.911768
## iter 10 value 719.129839
## iter 20 value 337.895992
## iter 30 value 290.918675
## iter 40 value 260.716694
## iter 50 value 243.587634
## iter 60 value 232.255370
## iter 70 value 226.087507
## iter 80 value 220.422940
## iter 90 value 219.105956
## iter 100 value 217.863117
## final value 217.863117
## stopped after 100 iterations
## # weights: 113
## initial value 1960.576520
## iter 10 value 713.343117
## iter 20 value 379.806513
## iter 30 value 266.146560
## iter 40 value 221.874955
## iter 50 value 201.016751
## iter 60 value 198.328612
## iter 70 value 193.580483
## iter 80 value 189.122449
## iter 90 value 184.309586
## iter 100 value 181.616964
## final value 181.616964
## stopped after 100 iterations
## # weights: 141
## initial value 3810.190370
## iter 10 value 351.560111
## iter 20 value 228.282500
## iter 30 value 201.863782
## iter 40 value 188.961980
## iter 50 value 174.154977
## iter 60 value 163.790464
## iter 70 value 157.387330
## iter 80 value 153.157556
## iter 90 value 138.513104
## iter 100 value 132.099631
## final value 132.099631
## stopped after 100 iterations
## # weights: 169
## initial value 2489.347514
## iter 10 value 321.000589
## iter 20 value 236.496352
## iter 30 value 196.729220
## iter 40 value 174.257498
## iter 50 value 146.734167
## iter 60 value 131.551084
## iter 70 value 118.508194
## iter 80 value 112.341937
## iter 90 value 108.438161
## iter 100 value 106.249486
## final value 106.249486
## stopped after 100 iterations
## # weights: 57
## initial value 2422.335854
## iter 10 value 630.934615
## iter 20 value 508.877383
## iter 30 value 408.381918
## iter 40 value 322.439665
## iter 50 value 293.530773
## iter 60 value 283.616491
## iter 70 value 281.305711
## iter 80 value 278.348050
## iter 90 value 277.408663
## iter 100 value 276.743436
## final value 276.743436
## stopped after 100 iterations
## # weights: 85
## initial value 3165.522137
## iter 10 value 726.612705
## iter 20 value 425.096621
## iter 30 value 339.229583
## iter 40 value 308.913915
## iter 50 value 278.908243
## iter 60 value 261.053261
## iter 70 value 255.953463
## iter 80 value 253.088976
## iter 90 value 251.968401
## iter 100 value 251.144246
## final value 251.144246
## stopped after 100 iterations
## # weights: 113
## initial value 2065.024489
## iter 10 value 428.890782
## iter 20 value 316.862378
## iter 30 value 273.679999
## iter 40 value 248.555395
## iter 50 value 236.906863
## iter 60 value 230.908491
## iter 70 value 227.220349
## iter 80 value 226.413406
## iter 90 value 224.149785
## iter 100 value 223.482350
## final value 223.482350
## stopped after 100 iterations
## # weights: 141
## initial value 2354.127243
## iter 10 value 328.586710
## iter 20 value 247.627423
## iter 30 value 230.421679
## iter 40 value 221.757617
## iter 50 value 209.328517
## iter 60 value 200.289013
## iter 70 value 197.456194
## iter 80 value 196.364439
## iter 90 value 195.624383
## iter 100 value 194.961605
## final value 194.961605
## stopped after 100 iterations
## # weights: 169
## initial value 2491.785810
## iter 10 value 331.118478
## iter 20 value 243.640865
## iter 30 value 212.709335
## iter 40 value 194.126403
## iter 50 value 179.650012
## iter 60 value 171.605366
## iter 70 value 165.308021
## iter 80 value 159.910930
## iter 90 value 156.327432
## iter 100 value 153.231347
## final value 153.231347
## stopped after 100 iterations
## # weights: 57
## initial value 1809.605653
## iter 10 value 418.376146
## iter 20 value 342.738409
## iter 30 value 318.434193
## iter 40 value 314.608281
## iter 50 value 313.351299
## iter 60 value 311.661322
## iter 70 value 310.213788
## iter 80 value 309.979872
## iter 90 value 309.773473
## iter 100 value 303.906004
## final value 303.906004
## stopped after 100 iterations
## # weights: 85
## initial value 4181.760856
## iter 10 value 555.947565
## iter 20 value 399.233018
## iter 30 value 359.412667
## iter 40 value 319.273021
## iter 50 value 296.489662
## iter 60 value 289.586125
## iter 70 value 283.778885
## iter 80 value 280.508972
## iter 90 value 278.254613
## iter 100 value 274.194164
## final value 274.194164
## stopped after 100 iterations
## # weights: 113
## initial value 1617.601633
## iter 10 value 312.397569
## iter 20 value 271.926177
## iter 30 value 240.118766
## iter 40 value 224.457591
## iter 50 value 213.421747
## iter 60 value 207.635386
## iter 70 value 205.468317
## iter 80 value 203.747255
## iter 90 value 199.248002
## iter 100 value 197.426800
## final value 197.426800
## stopped after 100 iterations
## # weights: 141
## initial value 2147.311514
## iter 10 value 443.034906
## iter 20 value 316.521420
## iter 30 value 273.141595
## iter 40 value 252.707672
## iter 50 value 230.233231
## iter 60 value 219.175327
## iter 70 value 215.517904
## iter 80 value 213.136918
## iter 90 value 209.837035
## iter 100 value 208.200982
## final value 208.200982
## stopped after 100 iterations
## # weights: 169
## initial value 3129.003012
## iter 10 value 385.287196
## iter 20 value 314.703797
## iter 30 value 265.351534
## iter 40 value 231.068670
## iter 50 value 207.239422
## iter 60 value 197.943374
## iter 70 value 190.423971
## iter 80 value 184.012393
## iter 90 value 176.980931
## iter 100 value 173.851735
## final value 173.851735
## stopped after 100 iterations
## # weights: 57
## initial value 1643.453741
## iter 10 value 425.725968
## iter 20 value 347.347752
## iter 30 value 321.319138
## iter 40 value 299.698616
## iter 50 value 286.850069
## iter 60 value 283.346730
## iter 70 value 278.349034
## iter 80 value 274.365399
## iter 90 value 272.484671
## iter 100 value 270.764193
## final value 270.764193
## stopped after 100 iterations
## # weights: 85
## initial value 2433.882190
## iter 10 value 310.102186
## iter 20 value 267.001976
## iter 30 value 253.370214
## iter 40 value 245.359943
## iter 50 value 239.130475
## iter 60 value 233.296549
## iter 70 value 224.396464
## iter 80 value 218.936888
## iter 90 value 213.821704
## iter 100 value 210.886611
## final value 210.886611
## stopped after 100 iterations
## # weights: 113
## initial value 1764.262645
## iter 10 value 380.452273
## iter 20 value 303.725358
## iter 30 value 261.281336
## iter 40 value 243.299932
## iter 50 value 235.766779
## iter 60 value 226.489412
## iter 70 value 215.915625
## iter 80 value 207.583963
## iter 90 value 202.754361
## iter 100 value 197.919807
## final value 197.919807
## stopped after 100 iterations
## # weights: 141
## initial value 1935.576620
## iter 10 value 477.677197
## iter 20 value 355.678682
## iter 30 value 285.593140
## iter 40 value 258.686535
## iter 50 value 244.873952
## iter 60 value 233.372500
## iter 70 value 228.776164
## iter 80 value 223.161577
## iter 90 value 218.748684
## iter 100 value 215.758577
## final value 215.758577
## stopped after 100 iterations
## # weights: 169
## initial value 2236.532223
## iter 10 value 319.697939
## iter 20 value 257.314110
## iter 30 value 224.253378
## iter 40 value 192.863724
## iter 50 value 172.402993
## iter 60 value 160.459234
## iter 70 value 149.508672
## iter 80 value 140.707939
## iter 90 value 134.996493
## iter 100 value 132.758791
## final value 132.758791
## stopped after 100 iterations
## # weights: 57
## initial value 2582.881346
## iter 10 value 466.137651
## iter 20 value 381.982017
## iter 30 value 326.506269
## iter 40 value 303.292955
## iter 50 value 284.323616
## iter 60 value 278.559669
## iter 70 value 274.618969
## iter 80 value 272.286505
## iter 90 value 269.375127
## iter 100 value 266.314729
## final value 266.314729
## stopped after 100 iterations
## # weights: 85
## initial value 2258.329974
## iter 10 value 507.555601
## iter 20 value 378.571185
## iter 30 value 293.470655
## iter 40 value 267.687597
## iter 50 value 256.965029
## iter 60 value 248.886916
## iter 70 value 242.283364
## iter 80 value 240.575563
## iter 90 value 239.188194
## iter 100 value 231.164281
## final value 231.164281
## stopped after 100 iterations
## # weights: 113
## initial value 3885.946751
## iter 10 value 436.890538
## iter 20 value 306.508229
## iter 30 value 257.896910
## iter 40 value 239.545943
## iter 50 value 229.622434
## iter 60 value 224.317718
## iter 70 value 221.266445
## iter 80 value 218.748979
## iter 90 value 216.577222
## iter 100 value 215.419733
## final value 215.419733
## stopped after 100 iterations
## # weights: 141
## initial value 2008.396543
## iter 10 value 559.874586
## iter 20 value 368.705947
## iter 30 value 269.452824
## iter 40 value 252.226903
## iter 50 value 243.976099
## iter 60 value 241.159655
## iter 70 value 234.952977
## iter 80 value 226.850098
## iter 90 value 220.342057
## iter 100 value 210.743318
## final value 210.743318
## stopped after 100 iterations
## # weights: 169
## initial value 1957.298750
## iter 10 value 316.568040
## iter 20 value 251.406163
## iter 30 value 212.063374
## iter 40 value 182.315965
## iter 50 value 159.819277
## iter 60 value 149.857240
## iter 70 value 145.564169
## iter 80 value 141.366636
## iter 90 value 139.279260
## iter 100 value 137.413707
## final value 137.413707
## stopped after 100 iterations
## # weights: 57
## initial value 2841.658456
## iter 10 value 390.453794
## iter 20 value 331.698094
## iter 30 value 305.916796
## iter 40 value 294.522661
## iter 50 value 285.511430
## iter 60 value 279.070053
## iter 70 value 275.535137
## iter 80 value 274.804064
## iter 90 value 274.721022
## iter 100 value 273.578078
## final value 273.578078
## stopped after 100 iterations
## # weights: 85
## initial value 1848.376613
## iter 10 value 397.489519
## iter 20 value 305.789095
## iter 30 value 281.361194
## iter 40 value 265.187615
## iter 50 value 250.039694
## iter 60 value 231.554720
## iter 70 value 221.125517
## iter 80 value 218.068988
## iter 90 value 214.500150
## iter 100 value 211.635006
## final value 211.635006
## stopped after 100 iterations
## # weights: 113
## initial value 2340.359954
## iter 10 value 1102.685154
## iter 20 value 1101.505469
## iter 20 value 1101.505469
## iter 20 value 1101.505468
## final value 1101.505468
## converged
## # weights: 141
## initial value 1852.656461
## iter 10 value 592.243141
## iter 20 value 309.266100
## iter 30 value 239.413606
## iter 40 value 224.796404
## iter 50 value 210.097737
## iter 60 value 202.079762
## iter 70 value 197.802118
## iter 80 value 194.806434
## iter 90 value 192.803777
## iter 100 value 189.363100
## final value 189.363100
## stopped after 100 iterations
## # weights: 169
## initial value 2080.370897
## iter 10 value 387.190767
## iter 20 value 261.385987
## iter 30 value 220.265278
## iter 40 value 199.981023
## iter 50 value 185.631005
## iter 60 value 175.953452
## iter 70 value 167.978728
## iter 80 value 162.891676
## iter 90 value 158.272889
## iter 100 value 154.580938
## final value 154.580938
## stopped after 100 iterations
## # weights: 57
## initial value 3059.996952
## iter 10 value 636.597370
## iter 20 value 401.077584
## iter 30 value 349.536749
## iter 40 value 334.905764
## iter 50 value 326.431237
## iter 60 value 318.532465
## iter 70 value 312.683882
## iter 80 value 302.009215
## iter 90 value 298.837195
## iter 100 value 295.263029
## final value 295.263029
## stopped after 100 iterations
## # weights: 85
## initial value 2626.447212
## iter 10 value 439.521679
## iter 20 value 309.249823
## iter 30 value 276.542985
## iter 40 value 264.742248
## iter 50 value 249.003372
## iter 60 value 235.304820
## iter 70 value 229.802393
## iter 80 value 223.624806
## iter 90 value 221.018713
## iter 100 value 219.445399
## final value 219.445399
## stopped after 100 iterations
## # weights: 113
## initial value 2153.164765
## iter 10 value 347.039045
## iter 20 value 261.661070
## iter 30 value 234.822542
## iter 40 value 225.114333
## iter 50 value 220.616030
## iter 60 value 218.639838
## iter 70 value 217.948221
## iter 80 value 215.924601
## iter 90 value 215.462166
## iter 100 value 215.257106
## final value 215.257106
## stopped after 100 iterations
## # weights: 141
## initial value 4405.428963
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## final value 278.241132
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## iter 10 value 375.044723
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## final value 222.826455
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## initial value 2469.249795
## iter 10 value 524.750149
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## iter 90 value 182.985250
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## final value 182.224701
## stopped after 100 iterations
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## initial value 1994.214301
## iter 10 value 528.459783
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## final value 178.300724
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## final value 151.164180
## stopped after 100 iterations
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## iter 10 value 609.328412
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## initial value 1858.828773
## iter 10 value 329.764828
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## iter 10 value 326.189244
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## initial value 3044.100467
## iter 10 value 271.405318
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## iter 40 value 210.075635
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## iter 90 value 164.842956
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## final value 162.940981
## stopped after 100 iterations
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## initial value 1864.529947
## iter 10 value 324.548088
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## iter 90 value 143.284812
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## final value 141.857921
## stopped after 100 iterations
## # weights: 57
## initial value 2587.131307
## iter 10 value 533.020794
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## final value 286.634989
## stopped after 100 iterations
## # weights: 85
## initial value 1846.552384
## iter 10 value 403.566913
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## final value 234.183767
## stopped after 100 iterations
## # weights: 113
## initial value 1659.524727
## iter 10 value 403.819888
## iter 20 value 269.449407
## iter 30 value 219.868286
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## final value 170.256192
## stopped after 100 iterations
## # weights: 141
## initial value 1486.933730
## iter 10 value 290.806715
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## iter 90 value 166.799875
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## final value 164.366850
## stopped after 100 iterations
## # weights: 169
## initial value 1723.724216
## iter 10 value 326.690419
## iter 20 value 252.604249
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## iter 40 value 204.770984
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## iter 60 value 181.586286
## iter 70 value 173.790693
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## iter 90 value 164.036482
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## final value 162.208720
## stopped after 100 iterations
## # weights: 57
## initial value 1937.216609
## iter 10 value 414.604138
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## iter 40 value 291.575300
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## iter 60 value 282.022154
## iter 70 value 276.715076
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## iter 90 value 276.476328
## iter 100 value 276.467748
## final value 276.467748
## stopped after 100 iterations
## # weights: 85
## initial value 2388.643687
## iter 10 value 421.968562
## iter 20 value 308.903212
## iter 30 value 280.114997
## iter 40 value 268.276942
## iter 50 value 262.543207
## iter 60 value 256.818136
## iter 70 value 251.229554
## iter 80 value 246.519083
## iter 90 value 243.061952
## iter 100 value 236.001436
## final value 236.001436
## stopped after 100 iterations
## # weights: 113
## initial value 1675.865114
## iter 10 value 494.614269
## iter 20 value 332.389151
## iter 30 value 291.428385
## iter 40 value 254.837266
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## iter 60 value 213.394804
## iter 70 value 203.394134
## iter 80 value 201.303747
## iter 90 value 199.159781
## iter 100 value 195.260298
## final value 195.260298
## stopped after 100 iterations
## # weights: 141
## initial value 1737.438000
## iter 10 value 345.181688
## iter 20 value 296.491821
## iter 30 value 263.771150
## iter 40 value 227.397239
## iter 50 value 212.220983
## iter 60 value 202.650888
## iter 70 value 195.807117
## iter 80 value 190.874298
## iter 90 value 188.265318
## iter 100 value 186.430116
## final value 186.430116
## stopped after 100 iterations
## # weights: 169
## initial value 2521.350953
## iter 10 value 460.597719
## iter 20 value 269.692062
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## iter 40 value 211.312176
## iter 50 value 197.311147
## iter 60 value 190.319904
## iter 70 value 184.036897
## iter 80 value 182.579201
## iter 90 value 180.938991
## iter 100 value 179.744064
## final value 179.744064
## stopped after 100 iterations
## # weights: 57
## initial value 2056.079700
## iter 10 value 463.948172
## iter 20 value 358.298494
## iter 30 value 333.931866
## iter 40 value 320.742720
## iter 50 value 305.029372
## iter 60 value 291.598039
## iter 70 value 287.086985
## iter 80 value 284.972063
## iter 90 value 281.916076
## iter 100 value 274.319193
## final value 274.319193
## stopped after 100 iterations
## # weights: 85
## initial value 2355.626468
## iter 10 value 362.602478
## iter 20 value 296.131601
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## iter 40 value 235.963005
## iter 50 value 226.830392
## iter 60 value 224.691478
## iter 70 value 222.369289
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## iter 100 value 218.342862
## final value 218.342862
## stopped after 100 iterations
## # weights: 113
## initial value 2313.978385
## iter 10 value 312.843622
## iter 20 value 259.912841
## iter 30 value 216.071948
## iter 40 value 191.158486
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## iter 90 value 164.528586
## iter 100 value 163.898970
## final value 163.898970
## stopped after 100 iterations
## # weights: 141
## initial value 1836.384506
## iter 10 value 457.741496
## iter 20 value 344.113192
## iter 30 value 256.904950
## iter 40 value 219.976656
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## iter 60 value 189.003170
## iter 70 value 182.256673
## iter 80 value 178.049287
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## iter 100 value 175.586705
## final value 175.586705
## stopped after 100 iterations
## # weights: 169
## initial value 2205.662528
## iter 10 value 342.067438
## iter 20 value 239.801260
## iter 30 value 203.525476
## iter 40 value 188.008039
## iter 50 value 175.793515
## iter 60 value 171.017605
## iter 70 value 166.991654
## iter 80 value 163.562028
## iter 90 value 162.227035
## iter 100 value 161.441042
## final value 161.441042
## stopped after 100 iterations
## # weights: 57
## initial value 3193.648217
## iter 10 value 503.686798
## iter 20 value 408.013332
## iter 30 value 361.913837
## iter 40 value 330.283835
## iter 50 value 316.568160
## iter 60 value 305.567988
## iter 70 value 294.752584
## iter 80 value 291.852385
## iter 90 value 287.591228
## iter 100 value 287.090501
## final value 287.090501
## stopped after 100 iterations
## # weights: 85
## initial value 2507.316969
## iter 10 value 794.045269
## iter 20 value 433.069328
## iter 30 value 308.454999
## iter 40 value 269.226385
## iter 50 value 257.571888
## iter 60 value 251.985931
## iter 70 value 246.654457
## iter 80 value 241.762504
## iter 90 value 239.074521
## iter 100 value 237.895893
## final value 237.895893
## stopped after 100 iterations
## # weights: 113
## initial value 2085.710607
## iter 10 value 539.898538
## iter 20 value 331.007938
## iter 30 value 286.005004
## iter 40 value 256.271012
## iter 50 value 244.882989
## iter 60 value 236.378509
## iter 70 value 229.088585
## iter 80 value 227.489404
## iter 90 value 226.469933
## iter 100 value 226.070548
## final value 226.070548
## stopped after 100 iterations
## # weights: 141
## initial value 2257.289524
## iter 10 value 320.974017
## iter 20 value 261.282375
## iter 30 value 217.718637
## iter 40 value 196.835151
## iter 50 value 188.198825
## iter 60 value 184.041738
## iter 70 value 181.919998
## iter 80 value 177.900103
## iter 90 value 171.827157
## iter 100 value 168.471008
## final value 168.471008
## stopped after 100 iterations
## # weights: 169
## initial value 3013.887084
## iter 10 value 356.677145
## iter 20 value 233.909949
## iter 30 value 203.331199
## iter 40 value 187.622041
## iter 50 value 171.155617
## iter 60 value 162.959479
## iter 70 value 160.417814
## iter 80 value 159.127049
## iter 90 value 153.220577
## iter 100 value 152.360201
## final value 152.360201
## stopped after 100 iterations
## # weights: 57
## initial value 2365.866608
## iter 10 value 439.575530
## iter 20 value 375.432139
## iter 30 value 346.360114
## iter 40 value 312.060942
## iter 50 value 292.659821
## iter 60 value 274.287150
## iter 70 value 263.401757
## iter 80 value 254.852104
## iter 90 value 254.174514
## iter 100 value 254.131284
## final value 254.131284
## stopped after 100 iterations
## # weights: 85
## initial value 2334.317222
## iter 10 value 445.502560
## iter 20 value 354.551858
## iter 30 value 312.538542
## iter 40 value 272.480627
## iter 50 value 250.465116
## iter 60 value 247.022416
## iter 70 value 246.358908
## iter 80 value 245.204170
## iter 90 value 241.094574
## iter 100 value 236.024821
## final value 236.024821
## stopped after 100 iterations
## # weights: 113
## initial value 2108.368698
## iter 10 value 577.841776
## iter 20 value 288.741560
## iter 30 value 260.079594
## iter 40 value 244.707053
## iter 50 value 228.521573
## iter 60 value 220.119161
## iter 70 value 216.465640
## iter 80 value 214.398299
## iter 90 value 213.338546
## iter 100 value 212.179214
## final value 212.179214
## stopped after 100 iterations
## # weights: 141
## initial value 2687.931106
## iter 10 value 396.760864
## iter 20 value 252.074482
## iter 30 value 213.923209
## iter 40 value 194.649353
## iter 50 value 185.613541
## iter 60 value 178.685487
## iter 70 value 171.994808
## iter 80 value 165.399428
## iter 90 value 161.910739
## iter 100 value 159.804210
## final value 159.804210
## stopped after 100 iterations
## # weights: 169
## initial value 2016.078358
## iter 10 value 304.336842
## iter 20 value 221.410662
## iter 30 value 191.043134
## iter 40 value 168.007400
## iter 50 value 151.762952
## iter 60 value 144.792765
## iter 70 value 140.114116
## iter 80 value 138.347275
## iter 90 value 137.181445
## iter 100 value 135.921327
## final value 135.921327
## stopped after 100 iterations
## # weights: 57
## initial value 3413.668584
## iter 10 value 690.060682
## iter 20 value 558.636994
## iter 30 value 485.287557
## iter 40 value 399.697105
## iter 50 value 330.988537
## iter 60 value 318.763989
## iter 70 value 310.932790
## iter 80 value 307.776798
## iter 90 value 303.748355
## iter 100 value 303.208377
## final value 303.208377
## stopped after 100 iterations
## # weights: 85
## initial value 3083.132601
## iter 10 value 648.181288
## iter 20 value 410.874044
## iter 30 value 303.793190
## iter 40 value 272.152015
## iter 50 value 247.960900
## iter 60 value 239.116909
## iter 70 value 232.401464
## iter 80 value 229.521068
## iter 90 value 227.075234
## iter 100 value 223.543550
## final value 223.543550
## stopped after 100 iterations
## # weights: 113
## initial value 2402.592048
## iter 10 value 344.291626
## iter 20 value 283.763994
## iter 30 value 248.621807
## iter 40 value 234.320172
## iter 50 value 223.146158
## iter 60 value 218.227182
## iter 70 value 214.682562
## iter 80 value 211.042884
## iter 90 value 206.951760
## iter 100 value 203.077237
## final value 203.077237
## stopped after 100 iterations
## # weights: 141
## initial value 2075.182355
## iter 10 value 306.168736
## iter 20 value 222.453516
## iter 30 value 200.518323
## iter 40 value 188.454875
## iter 50 value 174.995344
## iter 60 value 161.870997
## iter 70 value 156.489300
## iter 80 value 154.028189
## iter 90 value 151.625361
## iter 100 value 148.896546
## final value 148.896546
## stopped after 100 iterations
## # weights: 169
## initial value 1718.848008
## iter 10 value 342.685583
## iter 20 value 238.582027
## iter 30 value 193.236624
## iter 40 value 164.843919
## iter 50 value 139.884636
## iter 60 value 124.214240
## iter 70 value 112.145100
## iter 80 value 107.268129
## iter 90 value 104.034272
## iter 100 value 102.516430
## final value 102.516430
## stopped after 100 iterations
## # weights: 169
## initial value 2736.103576
## iter 10 value 783.534505
## iter 20 value 518.145396
## iter 30 value 364.521208
## iter 40 value 311.688235
## iter 50 value 291.156454
## iter 60 value 272.821528
## iter 70 value 261.542331
## iter 80 value 257.345797
## iter 90 value 254.332942
## iter 100 value 251.173859
## final value 251.173859
## stopped after 100 iterations
plot(nn.train)
best_hyperparameters = nn.train$bestTune
We see that the best hyper-parameter for this model are size = 6 and decay = 0.001. We use our model that has been trained with these hyper-parameters, to predict out test dataset. We try changing the threshold manually to see if we can improve the prediction, as we did in the previous.
threshold = 0.2
nnProb = predict(nn.train, newdata=test_data, type="prob")
nnPred = rep("NOT.QSO", nrow(test_data))
nnPred[which(nnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(nnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.215961
Not a bad profit 0.215961, but still not best that the decision trees one. Furthermore, we do not know if this ithreshold s the best one or not, so we compute the optimal threshold as in the previous ones.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = best_hyperparameters
j <- 0
for (threshold in seq(0.05, 0.5, 0.05)){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
nn.train <- train(class ~.,
method = "nnet",
data = train,
preProcess = c("center", "scale"),
MaxNWts = 1000,
maxit = 100,
tuneGrid = grid,
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
nnProb = predict(nn.train, test, type="prob")
nnPred = rep("NOT.QSO", nrow(test))
nnPred[which(nnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(nnPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 945.321781
## iter 10 value 96.950654
## iter 20 value 60.645659
## iter 30 value 49.548939
## iter 40 value 44.612871
## iter 50 value 41.606362
## iter 60 value 38.463452
## iter 70 value 37.266314
## iter 80 value 36.848567
## iter 90 value 34.427432
## iter 100 value 33.770300
## final value 33.770300
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1760.103598
## iter 10 value 107.984121
## iter 20 value 61.780408
## iter 30 value 36.294819
## iter 40 value 30.481176
## iter 50 value 28.652083
## iter 60 value 25.264643
## iter 70 value 22.359722
## iter 80 value 19.755607
## iter 90 value 18.269057
## iter 100 value 17.102593
## final value 17.102593
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1017.074802
## iter 10 value 97.891651
## iter 20 value 56.244287
## iter 30 value 41.679764
## iter 40 value 36.028021
## iter 50 value 29.844150
## iter 60 value 27.482600
## iter 70 value 26.667249
## iter 80 value 24.434679
## iter 90 value 23.974440
## iter 100 value 23.084343
## final value 23.084343
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1047.173722
## iter 10 value 107.403321
## iter 20 value 57.866114
## iter 30 value 38.362331
## iter 40 value 33.189471
## iter 50 value 32.060109
## iter 60 value 31.222758
## iter 70 value 30.055332
## iter 80 value 29.367803
## iter 90 value 28.171972
## iter 100 value 27.459463
## final value 27.459463
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1275.252336
## iter 10 value 111.589459
## iter 20 value 75.109715
## iter 30 value 57.117149
## iter 40 value 42.081503
## iter 50 value 37.192711
## iter 60 value 35.628121
## iter 70 value 34.781895
## iter 80 value 34.279869
## iter 90 value 33.916271
## iter 100 value 33.704603
## final value 33.704603
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1583.476666
## iter 10 value 164.304423
## iter 20 value 87.909596
## iter 30 value 57.018061
## iter 40 value 47.668804
## iter 50 value 40.398890
## iter 60 value 39.275685
## iter 70 value 38.372810
## iter 80 value 37.993569
## iter 90 value 37.642275
## iter 100 value 37.430802
## final value 37.430802
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 850.903836
## iter 10 value 110.802175
## iter 20 value 81.159590
## iter 30 value 48.585397
## iter 40 value 30.543666
## iter 50 value 26.196909
## iter 60 value 24.682898
## iter 70 value 23.905446
## iter 80 value 23.409151
## iter 90 value 23.131656
## iter 100 value 22.753765
## final value 22.753765
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 729.281609
## iter 10 value 156.325436
## iter 20 value 80.301493
## iter 30 value 45.680059
## iter 40 value 34.067559
## iter 50 value 25.769247
## iter 60 value 22.839934
## iter 70 value 21.063898
## iter 80 value 20.240371
## iter 90 value 19.812821
## iter 100 value 19.511026
## final value 19.511026
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 724.617370
## iter 10 value 103.521686
## iter 20 value 65.136906
## iter 30 value 49.053640
## iter 40 value 41.720926
## iter 50 value 37.539571
## iter 60 value 35.820666
## iter 70 value 34.643130
## iter 80 value 33.691922
## iter 90 value 33.366618
## iter 100 value 32.182917
## final value 32.182917
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 893.748246
## iter 10 value 81.312273
## iter 20 value 53.060974
## iter 30 value 35.698359
## iter 40 value 30.598553
## iter 50 value 26.470484
## iter 60 value 24.856827
## iter 70 value 23.965550
## iter 80 value 22.956973
## iter 90 value 22.505379
## iter 100 value 21.740434
## final value 21.740434
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1344.133430
## iter 10 value 118.591252
## iter 20 value 70.981706
## iter 30 value 44.348862
## iter 40 value 31.051318
## iter 50 value 22.494728
## iter 60 value 18.211471
## iter 70 value 16.591179
## iter 80 value 15.489150
## iter 90 value 14.794303
## iter 100 value 14.550403
## final value 14.550403
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 916.344782
## iter 10 value 127.773356
## iter 20 value 72.073798
## iter 30 value 48.704666
## iter 40 value 35.300776
## iter 50 value 29.502184
## iter 60 value 26.473324
## iter 70 value 25.402802
## iter 80 value 24.880071
## iter 90 value 24.560526
## iter 100 value 24.254785
## final value 24.254785
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 726.532063
## iter 10 value 108.422287
## iter 20 value 59.336032
## iter 30 value 32.927780
## iter 40 value 28.486629
## iter 50 value 26.021534
## iter 60 value 22.691711
## iter 70 value 21.709835
## iter 80 value 20.418472
## iter 90 value 19.722035
## iter 100 value 19.194119
## final value 19.194119
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1202.186691
## iter 10 value 134.077038
## iter 20 value 70.050700
## iter 30 value 45.354261
## iter 40 value 38.071880
## iter 50 value 36.261740
## iter 60 value 35.436318
## iter 70 value 33.210736
## iter 80 value 32.539654
## iter 90 value 32.017328
## iter 100 value 31.501918
## final value 31.501918
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 719.095959
## iter 10 value 121.874593
## iter 20 value 66.248922
## iter 30 value 42.497828
## iter 40 value 27.795275
## iter 50 value 20.212947
## iter 60 value 17.651156
## iter 70 value 14.467887
## iter 80 value 12.493363
## iter 90 value 10.740396
## iter 100 value 9.705110
## final value 9.705110
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1096.312949
## iter 10 value 109.842755
## iter 20 value 63.228603
## iter 30 value 44.965983
## iter 40 value 27.984692
## iter 50 value 20.446684
## iter 60 value 17.541095
## iter 70 value 15.597223
## iter 80 value 14.607388
## iter 90 value 13.360680
## iter 100 value 12.710037
## final value 12.710037
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 986.204221
## iter 10 value 112.519337
## iter 20 value 71.240579
## iter 30 value 54.242144
## iter 40 value 41.865712
## iter 50 value 35.098090
## iter 60 value 31.938105
## iter 70 value 29.845867
## iter 80 value 24.960941
## iter 90 value 21.740588
## iter 100 value 21.083500
## final value 21.083500
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1510.186610
## iter 10 value 193.080477
## iter 20 value 117.295007
## iter 30 value 92.982511
## iter 40 value 75.133095
## iter 50 value 62.682741
## iter 60 value 53.445209
## iter 70 value 49.681259
## iter 80 value 47.661991
## iter 90 value 46.782790
## iter 100 value 43.758140
## final value 43.758140
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1804.349462
## iter 10 value 137.133047
## iter 20 value 94.386570
## iter 30 value 70.836058
## iter 40 value 55.381618
## iter 50 value 49.770167
## iter 60 value 45.914020
## iter 70 value 44.811140
## iter 80 value 40.995810
## iter 90 value 38.572685
## iter 100 value 36.946763
## final value 36.946763
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 904.998518
## iter 10 value 130.250358
## iter 20 value 85.835158
## iter 30 value 44.905199
## iter 40 value 28.140874
## iter 50 value 20.696490
## iter 60 value 18.801289
## iter 70 value 17.838140
## iter 80 value 16.288881
## iter 90 value 14.674613
## iter 100 value 13.701506
## final value 13.701506
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1525.271431
## iter 10 value 187.199784
## iter 20 value 92.664257
## iter 30 value 58.998296
## iter 40 value 38.031952
## iter 50 value 30.333704
## iter 60 value 27.866310
## iter 70 value 25.876959
## iter 80 value 25.020377
## iter 90 value 24.397784
## iter 100 value 24.219800
## final value 24.219800
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 766.117880
## iter 10 value 92.643336
## iter 20 value 61.207067
## iter 30 value 33.771475
## iter 40 value 19.731389
## iter 50 value 15.828832
## iter 60 value 14.989753
## iter 70 value 14.170468
## iter 80 value 12.261019
## iter 90 value 10.029219
## iter 100 value 9.461530
## final value 9.461530
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1265.069839
## iter 10 value 134.101554
## iter 20 value 85.549343
## iter 30 value 69.484584
## iter 40 value 58.907689
## iter 50 value 55.371988
## iter 60 value 53.100256
## iter 70 value 50.680216
## iter 80 value 49.759944
## iter 90 value 48.304137
## iter 100 value 47.612027
## final value 47.612027
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1065.324186
## iter 10 value 168.405600
## iter 20 value 118.425815
## iter 30 value 77.022857
## iter 40 value 48.959538
## iter 50 value 26.857310
## iter 60 value 19.694880
## iter 70 value 18.435568
## iter 80 value 16.990796
## iter 90 value 16.129934
## iter 100 value 15.314269
## final value 15.314269
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1063.930200
## iter 10 value 90.277361
## iter 20 value 53.635758
## iter 30 value 34.784506
## iter 40 value 24.867058
## iter 50 value 18.548377
## iter 60 value 14.582522
## iter 70 value 12.743963
## iter 80 value 11.092757
## iter 90 value 9.771132
## iter 100 value 8.949954
## final value 8.949954
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1543.278680
## iter 10 value 108.932178
## iter 20 value 67.037740
## iter 30 value 48.215751
## iter 40 value 38.403620
## iter 50 value 32.473581
## iter 60 value 30.387143
## iter 70 value 28.116358
## iter 80 value 26.881915
## iter 90 value 25.597112
## iter 100 value 24.168051
## final value 24.168051
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 939.409660
## iter 10 value 179.226084
## iter 20 value 81.606853
## iter 30 value 40.432177
## iter 40 value 22.242051
## iter 50 value 17.924768
## iter 60 value 16.525211
## iter 70 value 15.921796
## iter 80 value 15.536936
## iter 90 value 14.408779
## iter 100 value 9.081317
## final value 9.081317
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1231.729750
## iter 10 value 113.143043
## iter 20 value 66.121779
## iter 30 value 34.260814
## iter 40 value 20.501191
## iter 50 value 17.169730
## iter 60 value 15.032130
## iter 70 value 13.796890
## iter 80 value 13.258160
## iter 90 value 12.436665
## iter 100 value 11.861248
## final value 11.861248
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 621.111229
## iter 10 value 166.868808
## iter 20 value 81.426101
## iter 30 value 52.285651
## iter 40 value 43.316417
## iter 50 value 37.991377
## iter 60 value 33.728667
## iter 70 value 27.326256
## iter 80 value 25.172212
## iter 90 value 23.197060
## iter 100 value 21.590129
## final value 21.590129
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1488.041994
## iter 10 value 147.067450
## iter 20 value 104.413688
## iter 30 value 79.160972
## iter 40 value 64.945575
## iter 50 value 54.898965
## iter 60 value 47.593490
## iter 70 value 46.194445
## iter 80 value 45.092629
## iter 90 value 39.697478
## iter 100 value 37.609373
## final value 37.609373
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1347.696008
## iter 10 value 144.091876
## iter 20 value 67.071711
## iter 30 value 46.027909
## iter 40 value 35.170743
## iter 50 value 31.611916
## iter 60 value 28.432311
## iter 70 value 27.888557
## iter 80 value 27.132675
## iter 90 value 26.752185
## iter 100 value 26.393955
## final value 26.393955
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1547.384140
## iter 10 value 105.424740
## iter 20 value 58.034810
## iter 30 value 41.867642
## iter 40 value 32.913488
## iter 50 value 27.481197
## iter 60 value 24.915112
## iter 70 value 23.275126
## iter 80 value 22.039494
## iter 90 value 20.766077
## iter 100 value 19.618121
## final value 19.618121
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 795.292653
## iter 10 value 108.586070
## iter 20 value 65.059477
## iter 30 value 41.760316
## iter 40 value 20.851112
## iter 50 value 15.987781
## iter 60 value 14.351687
## iter 70 value 12.270459
## iter 80 value 11.462916
## iter 90 value 11.168573
## iter 100 value 11.003173
## final value 11.003173
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1082.469459
## iter 10 value 117.465930
## iter 20 value 70.668365
## iter 30 value 60.989250
## iter 40 value 56.023674
## iter 50 value 53.639648
## iter 60 value 47.611157
## iter 70 value 44.281986
## iter 80 value 39.265653
## iter 90 value 37.664477
## iter 100 value 35.911993
## final value 35.911993
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1046.565457
## iter 10 value 126.650826
## iter 20 value 87.699296
## iter 30 value 56.042569
## iter 40 value 39.658634
## iter 50 value 34.182279
## iter 60 value 32.081485
## iter 70 value 28.915759
## iter 80 value 24.285200
## iter 90 value 22.814895
## iter 100 value 21.610969
## final value 21.610969
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1015.745777
## iter 10 value 179.868666
## iter 20 value 104.813698
## iter 30 value 74.216496
## iter 40 value 57.935359
## iter 50 value 49.655381
## iter 60 value 45.256037
## iter 70 value 42.406636
## iter 80 value 40.462237
## iter 90 value 39.305007
## iter 100 value 38.182957
## final value 38.182957
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 864.499296
## iter 10 value 109.342514
## iter 20 value 62.853219
## iter 30 value 34.127291
## iter 40 value 21.435639
## iter 50 value 19.329505
## iter 60 value 17.685764
## iter 70 value 16.108956
## iter 80 value 14.646257
## iter 90 value 14.017792
## iter 100 value 13.213192
## final value 13.213192
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 990.600178
## iter 10 value 105.129863
## iter 20 value 65.487473
## iter 30 value 35.315317
## iter 40 value 29.035759
## iter 50 value 26.398770
## iter 60 value 22.975013
## iter 70 value 20.736862
## iter 80 value 19.106579
## iter 90 value 15.963698
## iter 100 value 14.724686
## final value 14.724686
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1263.295810
## iter 10 value 94.722050
## iter 20 value 61.298761
## iter 30 value 41.848763
## iter 40 value 37.123515
## iter 50 value 35.880503
## iter 60 value 34.307144
## iter 70 value 28.813728
## iter 80 value 26.789888
## iter 90 value 21.695835
## iter 100 value 19.765778
## final value 19.765778
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 945.075385
## iter 10 value 122.341028
## iter 20 value 69.242092
## iter 30 value 38.549904
## iter 40 value 25.295318
## iter 50 value 21.417658
## iter 60 value 18.787293
## iter 70 value 17.696850
## iter 80 value 16.907467
## iter 90 value 15.539832
## iter 100 value 14.902598
## final value 14.902598
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 825.437346
## iter 10 value 113.175881
## iter 20 value 63.203402
## iter 30 value 45.424468
## iter 40 value 40.453050
## iter 50 value 38.772386
## iter 60 value 37.647873
## iter 70 value 36.772223
## iter 80 value 34.482195
## iter 90 value 33.424518
## iter 100 value 33.002873
## final value 33.002873
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 906.013037
## iter 10 value 147.831359
## iter 20 value 101.007794
## iter 30 value 78.210626
## iter 40 value 60.407255
## iter 50 value 56.420746
## iter 60 value 54.260837
## iter 70 value 52.466377
## iter 80 value 51.412580
## iter 90 value 50.724311
## iter 100 value 49.894407
## final value 49.894407
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1117.280570
## iter 10 value 114.639969
## iter 20 value 48.178326
## iter 30 value 24.077780
## iter 40 value 19.745141
## iter 50 value 17.299329
## iter 60 value 16.094221
## iter 70 value 15.779231
## iter 80 value 15.334953
## iter 90 value 14.770347
## iter 100 value 14.191959
## final value 14.191959
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 629.644050
## iter 10 value 84.276050
## iter 20 value 55.070495
## iter 30 value 35.423223
## iter 40 value 20.571317
## iter 50 value 17.401264
## iter 60 value 14.549082
## iter 70 value 12.346285
## iter 80 value 10.973452
## iter 90 value 10.058351
## iter 100 value 8.801828
## final value 8.801828
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 794.564690
## iter 10 value 111.345905
## iter 20 value 68.339023
## iter 30 value 41.798114
## iter 40 value 27.737017
## iter 50 value 15.370208
## iter 60 value 12.484111
## iter 70 value 11.350492
## iter 80 value 9.170244
## iter 90 value 8.523793
## iter 100 value 7.383713
## final value 7.383713
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 655.674115
## iter 10 value 74.636306
## iter 20 value 43.769769
## iter 30 value 29.940578
## iter 40 value 13.634790
## iter 50 value 7.939094
## iter 60 value 6.225670
## iter 70 value 5.351534
## iter 80 value 4.924876
## iter 90 value 4.240437
## iter 100 value 4.085335
## final value 4.085335
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 918.089233
## iter 10 value 96.783915
## iter 20 value 59.895364
## iter 30 value 48.269837
## iter 40 value 39.361823
## iter 50 value 32.174873
## iter 60 value 28.979478
## iter 70 value 26.100306
## iter 80 value 24.005142
## iter 90 value 22.016428
## iter 100 value 20.727026
## final value 20.727026
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 930.246048
## iter 10 value 93.342920
## iter 20 value 59.512720
## iter 30 value 32.163759
## iter 40 value 16.862988
## iter 50 value 14.994385
## iter 60 value 13.851395
## iter 70 value 13.300139
## iter 80 value 12.856177
## iter 90 value 12.345836
## iter 100 value 11.832519
## final value 11.832519
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1049.087926
## iter 10 value 122.235123
## iter 20 value 71.818516
## iter 30 value 54.523249
## iter 40 value 40.545645
## iter 50 value 33.252844
## iter 60 value 27.830018
## iter 70 value 25.394312
## iter 80 value 24.023974
## iter 90 value 22.448834
## iter 100 value 21.856033
## final value 21.856033
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 790.055073
## iter 10 value 121.360052
## iter 20 value 79.804124
## iter 30 value 45.057953
## iter 40 value 32.511805
## iter 50 value 26.377398
## iter 60 value 22.946149
## iter 70 value 20.239629
## iter 80 value 19.065235
## iter 90 value 17.944163
## iter 100 value 16.126390
## final value 16.126390
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 970.569908
## iter 10 value 137.821943
## iter 20 value 84.480142
## iter 30 value 54.071318
## iter 40 value 35.218656
## iter 50 value 25.022342
## iter 60 value 22.093983
## iter 70 value 20.527837
## iter 80 value 19.035254
## iter 90 value 17.991711
## iter 100 value 17.388469
## final value 17.388469
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 857.122895
## iter 10 value 112.955765
## iter 20 value 65.981988
## iter 30 value 44.673830
## iter 40 value 33.923742
## iter 50 value 26.107863
## iter 60 value 20.612536
## iter 70 value 17.257654
## iter 80 value 15.593173
## iter 90 value 14.900181
## iter 100 value 11.857550
## final value 11.857550
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 820.206764
## iter 10 value 105.387882
## iter 20 value 77.776368
## iter 30 value 56.974628
## iter 40 value 37.047874
## iter 50 value 24.225542
## iter 60 value 20.952749
## iter 70 value 18.692002
## iter 80 value 17.396350
## iter 90 value 16.276939
## iter 100 value 15.615315
## final value 15.615315
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1433.399586
## iter 10 value 159.238908
## iter 20 value 99.591267
## iter 30 value 82.978545
## iter 40 value 77.402938
## iter 50 value 72.832409
## iter 60 value 67.849866
## iter 70 value 64.348570
## iter 80 value 58.828497
## iter 90 value 57.854245
## iter 100 value 54.787331
## final value 54.787331
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 987.509030
## iter 10 value 88.514533
## iter 20 value 54.200786
## iter 30 value 38.648444
## iter 40 value 32.194655
## iter 50 value 29.190773
## iter 60 value 27.087961
## iter 70 value 23.511305
## iter 80 value 21.924533
## iter 90 value 21.212604
## iter 100 value 20.409518
## final value 20.409518
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 772.146722
## iter 10 value 100.857617
## iter 20 value 41.735814
## iter 30 value 19.671969
## iter 40 value 11.778960
## iter 50 value 8.722906
## iter 60 value 7.549774
## iter 70 value 5.901879
## iter 80 value 5.208977
## iter 90 value 4.594935
## iter 100 value 3.914009
## final value 3.914009
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1150.852770
## iter 10 value 85.826739
## iter 20 value 50.555533
## iter 30 value 30.750380
## iter 40 value 19.979199
## iter 50 value 16.147845
## iter 60 value 14.921549
## iter 70 value 14.174200
## iter 80 value 13.772738
## iter 90 value 13.594582
## iter 100 value 13.284513
## final value 13.284513
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1529.367951
## iter 10 value 227.960974
## iter 20 value 70.338167
## iter 30 value 40.834544
## iter 40 value 34.424791
## iter 50 value 30.721079
## iter 60 value 28.043757
## iter 70 value 25.769551
## iter 80 value 23.581292
## iter 90 value 22.340444
## iter 100 value 21.422372
## final value 21.422372
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 858.272746
## iter 10 value 152.093448
## iter 20 value 87.693466
## iter 30 value 54.612556
## iter 40 value 40.319850
## iter 50 value 30.433252
## iter 60 value 26.078599
## iter 70 value 22.558835
## iter 80 value 19.727132
## iter 90 value 18.910606
## iter 100 value 16.658418
## final value 16.658418
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1164.268753
## iter 10 value 105.911667
## iter 20 value 69.657753
## iter 30 value 41.826899
## iter 40 value 32.824017
## iter 50 value 31.553805
## iter 60 value 30.731561
## iter 70 value 29.252359
## iter 80 value 28.760363
## iter 90 value 26.485417
## iter 100 value 25.625569
## final value 25.625569
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1628.575200
## iter 10 value 108.637440
## iter 20 value 76.866372
## iter 30 value 50.361861
## iter 40 value 37.088628
## iter 50 value 33.291101
## iter 60 value 31.411074
## iter 70 value 29.593744
## iter 80 value 28.075063
## iter 90 value 27.366373
## iter 100 value 26.356028
## final value 26.356028
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 821.187275
## iter 10 value 84.706972
## iter 20 value 52.183064
## iter 30 value 35.040389
## iter 40 value 24.136453
## iter 50 value 19.734364
## iter 60 value 18.142733
## iter 70 value 17.553169
## iter 80 value 14.297605
## iter 90 value 12.060334
## iter 100 value 11.798192
## final value 11.798192
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 900.501444
## iter 10 value 105.326194
## iter 20 value 66.700860
## iter 30 value 50.145423
## iter 40 value 31.933196
## iter 50 value 24.720206
## iter 60 value 21.845564
## iter 70 value 20.288795
## iter 80 value 19.241288
## iter 90 value 18.729549
## iter 100 value 18.447342
## final value 18.447342
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1387.184905
## iter 10 value 93.827422
## iter 20 value 55.142365
## iter 30 value 37.167655
## iter 40 value 23.802584
## iter 50 value 19.367644
## iter 60 value 17.594530
## iter 70 value 16.934238
## iter 80 value 16.544787
## iter 90 value 16.130024
## iter 100 value 15.885580
## final value 15.885580
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1113.969072
## iter 10 value 117.194695
## iter 20 value 80.944543
## iter 30 value 64.666233
## iter 40 value 58.292848
## iter 50 value 53.349120
## iter 60 value 52.053568
## iter 70 value 51.397682
## iter 80 value 51.017198
## iter 90 value 50.383821
## iter 100 value 49.656314
## final value 49.656314
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 832.159033
## iter 10 value 135.536170
## iter 20 value 85.255523
## iter 30 value 58.268843
## iter 40 value 44.717632
## iter 50 value 41.103285
## iter 60 value 34.127733
## iter 70 value 31.136646
## iter 80 value 30.233400
## iter 90 value 29.725047
## iter 100 value 29.571545
## final value 29.571545
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 718.782016
## iter 10 value 112.448468
## iter 20 value 68.673320
## iter 30 value 51.957595
## iter 40 value 34.285967
## iter 50 value 21.334391
## iter 60 value 14.656721
## iter 70 value 10.887174
## iter 80 value 7.803961
## iter 90 value 5.450229
## iter 100 value 5.119959
## final value 5.119959
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1037.741447
## iter 10 value 132.381596
## iter 20 value 83.426971
## iter 30 value 49.893703
## iter 40 value 33.471776
## iter 50 value 27.149080
## iter 60 value 24.779460
## iter 70 value 23.501373
## iter 80 value 22.225034
## iter 90 value 21.513093
## iter 100 value 20.870395
## final value 20.870395
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 707.878954
## iter 10 value 88.924687
## iter 20 value 44.021352
## iter 30 value 22.546575
## iter 40 value 14.073584
## iter 50 value 10.222164
## iter 60 value 8.605604
## iter 70 value 7.808429
## iter 80 value 7.388818
## iter 90 value 6.558084
## iter 100 value 6.233506
## final value 6.233506
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 881.130072
## iter 10 value 163.360001
## iter 20 value 113.601296
## iter 30 value 92.656451
## iter 40 value 75.732075
## iter 50 value 65.244700
## iter 60 value 49.603902
## iter 70 value 38.489106
## iter 80 value 34.749973
## iter 90 value 32.837520
## iter 100 value 31.491817
## final value 31.491817
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1040.575960
## iter 10 value 113.693918
## iter 20 value 46.631831
## iter 30 value 26.826095
## iter 40 value 13.410903
## iter 50 value 9.020630
## iter 60 value 8.257545
## iter 70 value 7.701026
## iter 80 value 7.198997
## iter 90 value 6.961354
## iter 100 value 6.787776
## final value 6.787776
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 966.022366
## iter 10 value 147.568319
## iter 20 value 77.040944
## iter 30 value 61.318733
## iter 40 value 56.760400
## iter 50 value 46.568092
## iter 60 value 39.954470
## iter 70 value 38.212043
## iter 80 value 37.031358
## iter 90 value 35.302764
## iter 100 value 31.496809
## final value 31.496809
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 657.483646
## iter 10 value 166.380545
## iter 20 value 78.675254
## iter 30 value 62.167930
## iter 40 value 49.409712
## iter 50 value 44.130297
## iter 60 value 40.361649
## iter 70 value 38.549312
## iter 80 value 37.550233
## iter 90 value 36.704377
## iter 100 value 35.837077
## final value 35.837077
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 870.953838
## iter 10 value 100.673351
## iter 20 value 56.085717
## iter 30 value 34.377880
## iter 40 value 29.135115
## iter 50 value 27.163098
## iter 60 value 24.646777
## iter 70 value 22.563484
## iter 80 value 20.516403
## iter 90 value 18.850271
## iter 100 value 17.971176
## final value 17.971176
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 898.562320
## iter 10 value 116.345992
## iter 20 value 75.793279
## iter 30 value 49.697778
## iter 40 value 34.673164
## iter 50 value 31.329766
## iter 60 value 28.245584
## iter 70 value 27.005713
## iter 80 value 24.977459
## iter 90 value 24.414403
## iter 100 value 22.095783
## final value 22.095783
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1046.781887
## iter 10 value 89.228966
## iter 20 value 57.812835
## iter 30 value 41.141939
## iter 40 value 33.725527
## iter 50 value 31.167034
## iter 60 value 30.031723
## iter 70 value 27.420618
## iter 80 value 26.006448
## iter 90 value 25.016087
## iter 100 value 24.508638
## final value 24.508638
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1134.522039
## iter 10 value 106.310835
## iter 20 value 68.383443
## iter 30 value 47.111405
## iter 40 value 32.722540
## iter 50 value 21.528429
## iter 60 value 19.760643
## iter 70 value 18.773494
## iter 80 value 17.882377
## iter 90 value 17.253760
## iter 100 value 16.426560
## final value 16.426560
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1128.229515
## iter 10 value 118.600247
## iter 20 value 84.860937
## iter 30 value 66.811462
## iter 40 value 48.989787
## iter 50 value 41.578661
## iter 60 value 39.685349
## iter 70 value 38.995269
## iter 80 value 38.662343
## iter 90 value 38.150273
## iter 100 value 37.676993
## final value 37.676993
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 816.390042
## iter 10 value 109.767896
## iter 20 value 69.666206
## iter 30 value 49.391973
## iter 40 value 39.072634
## iter 50 value 32.818465
## iter 60 value 30.060976
## iter 70 value 27.971573
## iter 80 value 25.540839
## iter 90 value 24.279240
## iter 100 value 23.718784
## final value 23.718784
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 715.861348
## iter 10 value 135.593907
## iter 20 value 92.243849
## iter 30 value 63.482469
## iter 40 value 43.944051
## iter 50 value 36.294352
## iter 60 value 33.474719
## iter 70 value 29.110448
## iter 80 value 27.152079
## iter 90 value 26.704470
## iter 100 value 26.441882
## final value 26.441882
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 864.634041
## iter 10 value 122.000266
## iter 20 value 92.847189
## iter 30 value 66.146959
## iter 40 value 51.486952
## iter 50 value 45.858980
## iter 60 value 43.056355
## iter 70 value 38.197655
## iter 80 value 36.517957
## iter 90 value 35.418069
## iter 100 value 32.257245
## final value 32.257245
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 700.339788
## iter 10 value 147.757010
## iter 20 value 74.395289
## iter 30 value 47.840091
## iter 40 value 38.978477
## iter 50 value 36.526282
## iter 60 value 35.843048
## iter 70 value 35.369023
## iter 80 value 35.031489
## iter 90 value 34.055217
## iter 100 value 33.712478
## final value 33.712478
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 877.946540
## iter 10 value 126.803892
## iter 20 value 90.020019
## iter 30 value 68.367737
## iter 40 value 54.377888
## iter 50 value 48.650668
## iter 60 value 43.632334
## iter 70 value 41.138772
## iter 80 value 39.521051
## iter 90 value 38.843521
## iter 100 value 38.270876
## final value 38.270876
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1155.879962
## iter 10 value 187.171291
## iter 20 value 129.006002
## iter 30 value 102.851700
## iter 40 value 79.342928
## iter 50 value 63.192246
## iter 60 value 48.475063
## iter 70 value 40.326685
## iter 80 value 37.732761
## iter 90 value 36.195273
## iter 100 value 34.961927
## final value 34.961927
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1461.342001
## iter 10 value 84.341981
## iter 20 value 58.491325
## iter 30 value 36.871731
## iter 40 value 25.165599
## iter 50 value 22.729035
## iter 60 value 22.074332
## iter 70 value 21.572861
## iter 80 value 20.306153
## iter 90 value 19.153771
## iter 100 value 14.875741
## final value 14.875741
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 906.380879
## iter 10 value 91.270111
## iter 20 value 35.260929
## iter 30 value 15.962967
## iter 40 value 7.889245
## iter 50 value 5.108762
## iter 60 value 3.561081
## iter 70 value 2.943434
## iter 80 value 2.498578
## iter 90 value 2.169579
## iter 100 value 1.954088
## final value 1.954088
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1070.541738
## iter 10 value 80.152396
## iter 20 value 44.847990
## iter 30 value 22.376551
## iter 40 value 12.891001
## iter 50 value 7.308593
## iter 60 value 5.308938
## iter 70 value 4.220671
## iter 80 value 3.535550
## iter 90 value 3.290707
## iter 100 value 3.135588
## final value 3.135588
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 949.982548
## iter 10 value 84.387550
## iter 20 value 39.590786
## iter 30 value 20.424938
## iter 40 value 12.867072
## iter 50 value 11.460000
## iter 60 value 10.770028
## iter 70 value 10.392522
## iter 80 value 10.094348
## iter 90 value 9.796983
## iter 100 value 9.561152
## final value 9.561152
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 925.556158
## iter 10 value 81.922153
## iter 20 value 38.799164
## iter 30 value 23.163803
## iter 40 value 14.545384
## iter 50 value 12.946097
## iter 60 value 11.748857
## iter 70 value 8.305475
## iter 80 value 6.518022
## iter 90 value 5.803527
## iter 100 value 5.242296
## final value 5.242296
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 820.547411
## iter 10 value 130.453326
## iter 20 value 89.905224
## iter 30 value 59.146828
## iter 40 value 31.008624
## iter 50 value 16.536702
## iter 60 value 13.081004
## iter 70 value 11.519302
## iter 80 value 10.372295
## iter 90 value 9.394230
## iter 100 value 8.833388
## final value 8.833388
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 815.242150
## iter 10 value 119.673694
## iter 20 value 69.944722
## iter 30 value 43.291437
## iter 40 value 32.498994
## iter 50 value 28.656610
## iter 60 value 26.604281
## iter 70 value 23.357679
## iter 80 value 16.728343
## iter 90 value 15.776654
## iter 100 value 14.965709
## final value 14.965709
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 817.737169
## iter 10 value 102.168658
## iter 20 value 63.893914
## iter 30 value 48.077588
## iter 40 value 41.380369
## iter 50 value 39.123456
## iter 60 value 33.965638
## iter 70 value 30.883142
## iter 80 value 29.196202
## iter 90 value 28.427488
## iter 100 value 27.103694
## final value 27.103694
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 824.555013
## iter 10 value 141.631406
## iter 20 value 83.196923
## iter 30 value 56.635908
## iter 40 value 40.769365
## iter 50 value 30.734144
## iter 60 value 25.723324
## iter 70 value 23.945191
## iter 80 value 22.778379
## iter 90 value 21.073402
## iter 100 value 20.078841
## final value 20.078841
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1195.641689
## iter 10 value 78.485106
## iter 20 value 45.450241
## iter 30 value 31.115441
## iter 40 value 20.379933
## iter 50 value 12.649322
## iter 60 value 10.233460
## iter 70 value 9.579823
## iter 80 value 9.276064
## iter 90 value 9.029974
## iter 100 value 8.441181
## final value 8.441181
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 691.952723
## iter 10 value 95.946493
## iter 20 value 57.068203
## iter 30 value 38.259698
## iter 40 value 32.056749
## iter 50 value 25.828245
## iter 60 value 22.262509
## iter 70 value 18.968149
## iter 80 value 15.319485
## iter 90 value 12.245900
## iter 100 value 11.083651
## final value 11.083651
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1604.380800
## iter 10 value 236.898223
## iter 20 value 118.048771
## iter 30 value 94.763184
## iter 40 value 80.482845
## iter 50 value 74.229049
## iter 60 value 69.880160
## iter 70 value 67.906165
## iter 80 value 66.212871
## iter 90 value 64.609649
## iter 100 value 61.662822
## final value 61.662822
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 932.356632
## iter 10 value 161.178568
## iter 20 value 109.951579
## iter 30 value 85.357595
## iter 40 value 55.183059
## iter 50 value 41.002278
## iter 60 value 35.418137
## iter 70 value 31.514229
## iter 80 value 28.457133
## iter 90 value 27.353661
## iter 100 value 25.024541
## final value 25.024541
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 826.617127
## iter 10 value 117.000206
## iter 20 value 63.621486
## iter 30 value 41.886112
## iter 40 value 29.085219
## iter 50 value 25.652687
## iter 60 value 24.154009
## iter 70 value 22.923466
## iter 80 value 22.179288
## iter 90 value 21.207882
## iter 100 value 20.253644
## final value 20.253644
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1062.193656
## iter 10 value 111.750725
## iter 20 value 72.033417
## iter 30 value 46.050246
## iter 40 value 29.147736
## iter 50 value 22.937211
## iter 60 value 21.514898
## iter 70 value 19.771905
## iter 80 value 17.549459
## iter 90 value 16.366356
## iter 100 value 15.776198
## final value 15.776198
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 645.388315
## iter 10 value 180.545239
## iter 20 value 97.220502
## iter 30 value 57.226140
## iter 40 value 38.735730
## iter 50 value 32.089735
## iter 60 value 25.718674
## iter 70 value 22.588270
## iter 80 value 19.963041
## iter 90 value 17.565713
## iter 100 value 13.775011
## final value 13.775011
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 603.559355
## iter 10 value 120.507775
## iter 20 value 64.648961
## iter 30 value 41.887640
## iter 40 value 27.159530
## iter 50 value 20.509719
## iter 60 value 18.134474
## iter 70 value 15.976666
## iter 80 value 15.598093
## iter 90 value 15.334394
## iter 100 value 15.012022
## final value 15.012022
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1166.260548
## iter 10 value 185.338428
## iter 20 value 130.371435
## iter 30 value 100.364056
## iter 40 value 78.044738
## iter 50 value 58.935708
## iter 60 value 47.077090
## iter 70 value 40.151727
## iter 80 value 35.622018
## iter 90 value 31.515695
## iter 100 value 29.206763
## final value 29.206763
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 716.428608
## iter 10 value 112.221457
## iter 20 value 75.296103
## iter 30 value 56.865931
## iter 40 value 49.922416
## iter 50 value 48.112588
## iter 60 value 47.377264
## iter 70 value 40.424392
## iter 80 value 37.772111
## iter 90 value 36.882748
## iter 100 value 36.227910
## final value 36.227910
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 742.355932
## iter 10 value 126.822265
## iter 20 value 69.110499
## iter 30 value 42.841958
## iter 40 value 30.686699
## iter 50 value 24.702871
## iter 60 value 21.887224
## iter 70 value 19.471906
## iter 80 value 17.966946
## iter 90 value 16.924108
## iter 100 value 15.153771
## final value 15.153771
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1614.763688
## iter 10 value 164.302309
## iter 20 value 125.011640
## iter 30 value 98.388671
## iter 40 value 64.634561
## iter 50 value 47.354042
## iter 60 value 42.285463
## iter 70 value 39.859470
## iter 80 value 38.503516
## iter 90 value 36.258227
## iter 100 value 33.614772
## final value 33.614772
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1506.265280
## iter 10 value 118.830364
## iter 20 value 87.983356
## iter 30 value 61.407079
## iter 40 value 47.126665
## iter 50 value 36.899024
## iter 60 value 32.555443
## iter 70 value 30.481253
## iter 80 value 28.141992
## iter 90 value 26.332175
## iter 100 value 24.323719
## final value 24.323719
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 677.682140
## iter 10 value 157.528615
## iter 20 value 108.571035
## iter 30 value 91.468412
## iter 40 value 80.260744
## iter 50 value 72.167986
## iter 60 value 65.190795
## iter 70 value 59.544566
## iter 80 value 51.876665
## iter 90 value 46.700043
## iter 100 value 42.025288
## final value 42.025288
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 922.532703
## iter 10 value 153.117253
## iter 20 value 92.945861
## iter 30 value 71.317482
## iter 40 value 57.704889
## iter 50 value 49.943288
## iter 60 value 46.675904
## iter 70 value 43.593047
## iter 80 value 42.546506
## iter 90 value 42.022950
## iter 100 value 41.511458
## final value 41.511458
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 645.978084
## iter 10 value 99.088157
## iter 20 value 49.350753
## iter 30 value 28.588498
## iter 40 value 23.834823
## iter 50 value 22.042804
## iter 60 value 19.032037
## iter 70 value 18.050364
## iter 80 value 17.470242
## iter 90 value 17.145864
## iter 100 value 16.722103
## final value 16.722103
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 944.008367
## iter 10 value 68.385734
## iter 20 value 45.223946
## iter 30 value 21.960801
## iter 40 value 10.953732
## iter 50 value 6.342514
## iter 60 value 4.099181
## iter 70 value 3.131405
## iter 80 value 2.858056
## iter 90 value 2.694577
## iter 100 value 2.555446
## final value 2.555446
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1218.295285
## iter 10 value 145.827025
## iter 20 value 102.850889
## iter 30 value 62.401246
## iter 40 value 44.066172
## iter 50 value 30.705109
## iter 60 value 22.938987
## iter 70 value 20.159206
## iter 80 value 15.871789
## iter 90 value 14.682006
## iter 100 value 13.993177
## final value 13.993177
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 923.424631
## iter 10 value 127.729974
## iter 20 value 65.962343
## iter 30 value 46.893119
## iter 40 value 32.502869
## iter 50 value 24.104154
## iter 60 value 20.444030
## iter 70 value 17.924559
## iter 80 value 16.633230
## iter 90 value 15.837525
## iter 100 value 15.238200
## final value 15.238200
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 993.945386
## iter 10 value 81.165339
## iter 20 value 48.470137
## iter 30 value 33.021472
## iter 40 value 25.926259
## iter 50 value 22.534592
## iter 60 value 20.888732
## iter 70 value 19.385881
## iter 80 value 18.736642
## iter 90 value 18.300653
## iter 100 value 17.819841
## final value 17.819841
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 831.187512
## iter 10 value 104.389089
## iter 20 value 60.889862
## iter 30 value 43.420546
## iter 40 value 32.575918
## iter 50 value 26.200542
## iter 60 value 23.336465
## iter 70 value 21.734279
## iter 80 value 19.916509
## iter 90 value 17.827335
## iter 100 value 17.332274
## final value 17.332274
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 755.709544
## iter 10 value 94.252761
## iter 20 value 61.708372
## iter 30 value 36.925710
## iter 40 value 30.407844
## iter 50 value 27.852375
## iter 60 value 25.317158
## iter 70 value 24.329904
## iter 80 value 23.028433
## iter 90 value 22.597188
## iter 100 value 22.282799
## final value 22.282799
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1177.289757
## iter 10 value 112.884200
## iter 20 value 67.775096
## iter 30 value 50.003517
## iter 40 value 35.242288
## iter 50 value 27.726456
## iter 60 value 25.339824
## iter 70 value 23.979932
## iter 80 value 23.224138
## iter 90 value 22.846024
## iter 100 value 22.329524
## final value 22.329524
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1047.222105
## iter 10 value 119.389914
## iter 20 value 73.655107
## iter 30 value 46.345349
## iter 40 value 24.811355
## iter 50 value 13.584146
## iter 60 value 10.711174
## iter 70 value 8.575644
## iter 80 value 6.660931
## iter 90 value 5.562350
## iter 100 value 4.727481
## final value 4.727481
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 773.079927
## iter 10 value 97.248853
## iter 20 value 62.848430
## iter 30 value 43.613736
## iter 40 value 33.464397
## iter 50 value 28.202278
## iter 60 value 24.369545
## iter 70 value 20.748755
## iter 80 value 19.784936
## iter 90 value 16.664456
## iter 100 value 14.476549
## final value 14.476549
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 732.350180
## iter 10 value 148.820004
## iter 20 value 73.074299
## iter 30 value 56.970587
## iter 40 value 50.706242
## iter 50 value 45.591117
## iter 60 value 42.682270
## iter 70 value 37.375596
## iter 80 value 34.076638
## iter 90 value 31.828936
## iter 100 value 28.910659
## final value 28.910659
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1078.832455
## iter 10 value 141.952426
## iter 20 value 101.439650
## iter 30 value 86.655160
## iter 40 value 73.972135
## iter 50 value 66.256948
## iter 60 value 61.628316
## iter 70 value 57.240427
## iter 80 value 55.712253
## iter 90 value 53.852523
## iter 100 value 52.519277
## final value 52.519277
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 827.464673
## iter 10 value 129.883637
## iter 20 value 95.448400
## iter 30 value 72.419822
## iter 40 value 50.961204
## iter 50 value 43.425755
## iter 60 value 38.701625
## iter 70 value 35.733188
## iter 80 value 33.700607
## iter 90 value 32.581558
## iter 100 value 31.415884
## final value 31.415884
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1576.175478
## iter 10 value 109.427444
## iter 20 value 62.336293
## iter 30 value 42.928109
## iter 40 value 26.465107
## iter 50 value 17.474250
## iter 60 value 13.113873
## iter 70 value 11.661959
## iter 80 value 10.663456
## iter 90 value 9.686482
## iter 100 value 9.226207
## final value 9.226207
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 613.053629
## iter 10 value 98.935071
## iter 20 value 47.579567
## iter 30 value 39.024167
## iter 40 value 34.085775
## iter 50 value 31.040375
## iter 60 value 29.163527
## iter 70 value 26.849034
## iter 80 value 25.512784
## iter 90 value 23.659919
## iter 100 value 21.317521
## final value 21.317521
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1229.757409
## iter 10 value 181.927523
## iter 20 value 108.672832
## iter 30 value 72.079889
## iter 40 value 49.675051
## iter 50 value 34.527734
## iter 60 value 29.396889
## iter 70 value 27.134588
## iter 80 value 25.353590
## iter 90 value 24.718528
## iter 100 value 24.070007
## final value 24.070007
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 888.207914
## iter 10 value 136.044243
## iter 20 value 93.284412
## iter 30 value 74.460801
## iter 40 value 59.911824
## iter 50 value 54.207848
## iter 60 value 46.101918
## iter 70 value 37.218013
## iter 80 value 34.564615
## iter 90 value 33.066055
## iter 100 value 32.121263
## final value 32.121263
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1436.644886
## iter 10 value 146.101513
## iter 20 value 85.491253
## iter 30 value 71.915578
## iter 40 value 55.738751
## iter 50 value 46.244657
## iter 60 value 38.989397
## iter 70 value 35.800343
## iter 80 value 34.533407
## iter 90 value 32.319501
## iter 100 value 30.808654
## final value 30.808654
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 753.682973
## iter 10 value 93.082129
## iter 20 value 57.124521
## iter 30 value 33.828272
## iter 40 value 27.380808
## iter 50 value 25.491269
## iter 60 value 23.580446
## iter 70 value 20.255790
## iter 80 value 15.161506
## iter 90 value 12.512312
## iter 100 value 11.716445
## final value 11.716445
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 715.660602
## iter 10 value 102.155412
## iter 20 value 73.100171
## iter 30 value 34.315602
## iter 40 value 16.627930
## iter 50 value 11.887679
## iter 60 value 10.805730
## iter 70 value 9.915931
## iter 80 value 9.507121
## iter 90 value 8.922459
## iter 100 value 7.037662
## final value 7.037662
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 649.590749
## iter 10 value 112.375792
## iter 20 value 60.166298
## iter 30 value 30.974368
## iter 40 value 17.664442
## iter 50 value 11.958450
## iter 60 value 9.130588
## iter 70 value 8.241870
## iter 80 value 7.841109
## iter 90 value 7.544602
## iter 100 value 7.380742
## final value 7.380742
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 840.598890
## iter 10 value 234.974477
## iter 20 value 118.219281
## iter 30 value 81.750054
## iter 40 value 56.765450
## iter 50 value 35.990731
## iter 60 value 27.756293
## iter 70 value 23.357136
## iter 80 value 21.213532
## iter 90 value 19.242396
## iter 100 value 17.380563
## final value 17.380563
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 787.027853
## iter 10 value 84.380069
## iter 20 value 49.502842
## iter 30 value 27.695008
## iter 40 value 15.965375
## iter 50 value 14.430851
## iter 60 value 13.622529
## iter 70 value 13.143668
## iter 80 value 9.684805
## iter 90 value 8.816235
## iter 100 value 8.454585
## final value 8.454585
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 861.608801
## iter 10 value 138.889012
## iter 20 value 72.032827
## iter 30 value 41.968253
## iter 40 value 25.122994
## iter 50 value 20.150552
## iter 60 value 18.947220
## iter 70 value 18.139375
## iter 80 value 17.804070
## iter 90 value 17.504326
## iter 100 value 17.257857
## final value 17.257857
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 995.300423
## iter 10 value 186.893163
## iter 20 value 97.854591
## iter 30 value 73.374906
## iter 40 value 64.788632
## iter 50 value 59.665118
## iter 60 value 54.631395
## iter 70 value 52.214099
## iter 80 value 47.765820
## iter 90 value 45.271078
## iter 100 value 33.975397
## final value 33.975397
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1160.675845
## iter 10 value 92.875939
## iter 20 value 63.091693
## iter 30 value 30.874431
## iter 40 value 21.762384
## iter 50 value 20.447325
## iter 60 value 19.694345
## iter 70 value 19.203348
## iter 80 value 18.886012
## iter 90 value 18.285857
## iter 100 value 16.371137
## final value 16.371137
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 737.402102
## iter 10 value 79.257429
## iter 20 value 43.971932
## iter 30 value 27.949245
## iter 40 value 17.334897
## iter 50 value 13.576341
## iter 60 value 11.961109
## iter 70 value 10.341763
## iter 80 value 8.933748
## iter 90 value 7.705516
## iter 100 value 6.973034
## final value 6.973034
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 704.602572
## iter 10 value 102.894446
## iter 20 value 59.168650
## iter 30 value 34.511790
## iter 40 value 18.960682
## iter 50 value 14.712304
## iter 60 value 13.552067
## iter 70 value 12.442884
## iter 80 value 11.965562
## iter 90 value 10.618444
## iter 100 value 9.979084
## final value 9.979084
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 914.208736
## iter 10 value 112.033373
## iter 20 value 81.585356
## iter 30 value 55.381634
## iter 40 value 40.152436
## iter 50 value 30.614881
## iter 60 value 25.324942
## iter 70 value 23.063367
## iter 80 value 21.830864
## iter 90 value 21.144979
## iter 100 value 20.489971
## final value 20.489971
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1333.148196
## iter 10 value 181.431187
## iter 20 value 107.860688
## iter 30 value 62.825160
## iter 40 value 39.951673
## iter 50 value 24.279314
## iter 60 value 17.902915
## iter 70 value 16.026008
## iter 80 value 14.903508
## iter 90 value 13.468027
## iter 100 value 11.451627
## final value 11.451627
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 978.656919
## iter 10 value 145.357072
## iter 20 value 96.621862
## iter 30 value 60.809392
## iter 40 value 45.668271
## iter 50 value 30.181807
## iter 60 value 26.401958
## iter 70 value 23.181556
## iter 80 value 21.021688
## iter 90 value 19.472652
## iter 100 value 18.731741
## final value 18.731741
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 798.566107
## iter 10 value 158.431347
## iter 20 value 91.681868
## iter 30 value 67.349133
## iter 40 value 58.457492
## iter 50 value 53.565173
## iter 60 value 50.619327
## iter 70 value 47.961198
## iter 80 value 47.405078
## iter 90 value 46.930580
## iter 100 value 45.763304
## final value 45.763304
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1188.667863
## iter 10 value 153.230267
## iter 20 value 106.184731
## iter 30 value 90.749785
## iter 40 value 78.769592
## iter 50 value 68.691305
## iter 60 value 61.983852
## iter 70 value 59.447108
## iter 80 value 57.727944
## iter 90 value 55.132388
## iter 100 value 53.922216
## final value 53.922216
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1122.104597
## iter 10 value 120.971564
## iter 20 value 75.800887
## iter 30 value 49.732204
## iter 40 value 40.351898
## iter 50 value 37.097680
## iter 60 value 36.296842
## iter 70 value 35.826253
## iter 80 value 35.173754
## iter 90 value 34.879467
## iter 100 value 34.428316
## final value 34.428316
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 903.115236
## iter 10 value 131.778904
## iter 20 value 63.391824
## iter 30 value 46.171920
## iter 40 value 39.463071
## iter 50 value 36.674040
## iter 60 value 35.296644
## iter 70 value 34.981894
## iter 80 value 34.576046
## iter 90 value 34.239284
## iter 100 value 34.038534
## final value 34.038534
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1286.390369
## iter 10 value 144.888257
## iter 20 value 89.724827
## iter 30 value 75.722414
## iter 40 value 68.702883
## iter 50 value 62.546117
## iter 60 value 59.362883
## iter 70 value 56.232066
## iter 80 value 55.216430
## iter 90 value 54.069499
## iter 100 value 49.643543
## final value 49.643543
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 666.035276
## iter 10 value 106.865097
## iter 20 value 64.033587
## iter 30 value 30.360858
## iter 40 value 14.623682
## iter 50 value 11.968536
## iter 60 value 10.134548
## iter 70 value 7.984359
## iter 80 value 6.514712
## iter 90 value 4.893438
## iter 100 value 4.526645
## final value 4.526645
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 686.281595
## iter 10 value 84.742493
## iter 20 value 46.851800
## iter 30 value 20.518266
## iter 40 value 10.874221
## iter 50 value 8.957403
## iter 60 value 8.216497
## iter 70 value 6.981983
## iter 80 value 5.656335
## iter 90 value 4.914416
## iter 100 value 4.676112
## final value 4.676112
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1084.723798
## iter 10 value 84.188648
## iter 20 value 54.845948
## iter 30 value 22.085309
## iter 40 value 10.169861
## iter 50 value 4.847787
## iter 60 value 3.397722
## iter 70 value 2.945480
## iter 80 value 2.675276
## iter 90 value 2.496898
## iter 100 value 2.375023
## final value 2.375023
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 959.171493
## iter 10 value 83.131529
## iter 20 value 52.984075
## iter 30 value 30.910165
## iter 40 value 21.804971
## iter 50 value 20.086645
## iter 60 value 17.346679
## iter 70 value 16.674136
## iter 80 value 16.086003
## iter 90 value 15.198299
## iter 100 value 11.314348
## final value 11.314348
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1090.931856
## iter 10 value 104.016970
## iter 20 value 60.737673
## iter 30 value 25.427618
## iter 40 value 17.195010
## iter 50 value 12.855602
## iter 60 value 11.036595
## iter 70 value 9.898588
## iter 80 value 9.313441
## iter 90 value 8.715665
## iter 100 value 7.930333
## final value 7.930333
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 976.546279
## iter 10 value 123.683235
## iter 20 value 77.848664
## iter 30 value 52.902549
## iter 40 value 40.310031
## iter 50 value 32.984056
## iter 60 value 29.760334
## iter 70 value 27.488756
## iter 80 value 24.662077
## iter 90 value 23.741821
## iter 100 value 21.368016
## final value 21.368016
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 998.798361
## iter 10 value 115.808912
## iter 20 value 93.361564
## iter 30 value 63.192752
## iter 40 value 49.958051
## iter 50 value 44.166815
## iter 60 value 41.548705
## iter 70 value 38.179062
## iter 80 value 34.323105
## iter 90 value 31.761987
## iter 100 value 28.922786
## final value 28.922786
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 750.091430
## iter 10 value 99.096386
## iter 20 value 59.471262
## iter 30 value 31.856104
## iter 40 value 20.580800
## iter 50 value 18.443301
## iter 60 value 17.822805
## iter 70 value 15.741951
## iter 80 value 9.692889
## iter 90 value 8.447048
## iter 100 value 7.506300
## final value 7.506300
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 975.600894
## iter 10 value 93.797703
## iter 20 value 49.863608
## iter 30 value 40.470062
## iter 40 value 33.697047
## iter 50 value 28.967731
## iter 60 value 25.429169
## iter 70 value 23.045919
## iter 80 value 21.245629
## iter 90 value 19.104621
## iter 100 value 17.736255
## final value 17.736255
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 801.042183
## iter 10 value 132.670033
## iter 20 value 86.287939
## iter 30 value 62.696203
## iter 40 value 46.292177
## iter 50 value 42.354139
## iter 60 value 39.662492
## iter 70 value 37.271452
## iter 80 value 33.872712
## iter 90 value 33.265646
## iter 100 value 32.754973
## final value 32.754973
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1203.159573
## iter 10 value 130.788378
## iter 20 value 87.501263
## iter 30 value 71.661790
## iter 40 value 63.307965
## iter 50 value 54.422444
## iter 60 value 46.357776
## iter 70 value 44.200664
## iter 80 value 42.950954
## iter 90 value 42.289999
## iter 100 value 41.736233
## final value 41.736233
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 970.089165
## iter 10 value 156.203233
## iter 20 value 96.665339
## iter 30 value 81.102619
## iter 40 value 72.213170
## iter 50 value 62.999667
## iter 60 value 53.560885
## iter 70 value 49.154970
## iter 80 value 43.368304
## iter 90 value 37.116930
## iter 100 value 34.458463
## final value 34.458463
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 704.466523
## iter 10 value 101.946212
## iter 20 value 53.102889
## iter 30 value 31.103179
## iter 40 value 19.267053
## iter 50 value 14.722895
## iter 60 value 11.688456
## iter 70 value 7.687743
## iter 80 value 5.196295
## iter 90 value 3.909782
## iter 100 value 3.558805
## final value 3.558805
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 742.948883
## iter 10 value 99.179688
## iter 20 value 43.181433
## iter 30 value 20.737812
## iter 40 value 7.861015
## iter 50 value 5.353677
## iter 60 value 4.702161
## iter 70 value 4.082504
## iter 80 value 3.713872
## iter 90 value 3.447340
## iter 100 value 3.270863
## final value 3.270863
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 748.322230
## iter 10 value 134.607272
## iter 20 value 93.058927
## iter 30 value 71.143947
## iter 40 value 49.459265
## iter 50 value 31.621733
## iter 60 value 24.038138
## iter 70 value 21.672127
## iter 80 value 20.445294
## iter 90 value 18.901763
## iter 100 value 16.860606
## final value 16.860606
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 694.556065
## iter 10 value 127.163651
## iter 20 value 62.619060
## iter 30 value 35.505099
## iter 40 value 18.224546
## iter 50 value 14.459912
## iter 60 value 13.343999
## iter 70 value 12.186256
## iter 80 value 11.362563
## iter 90 value 11.087205
## iter 100 value 10.373127
## final value 10.373127
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 667.781273
## iter 10 value 119.848110
## iter 20 value 64.575729
## iter 30 value 39.382564
## iter 40 value 19.519933
## iter 50 value 13.854357
## iter 60 value 12.437196
## iter 70 value 11.346226
## iter 80 value 10.217850
## iter 90 value 9.477542
## iter 100 value 9.046834
## final value 9.046834
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1930.430450
## iter 10 value 125.943249
## iter 20 value 78.065567
## iter 30 value 53.276498
## iter 40 value 38.765260
## iter 50 value 29.468935
## iter 60 value 25.478139
## iter 70 value 24.036576
## iter 80 value 23.274401
## iter 90 value 22.587743
## iter 100 value 20.702224
## final value 20.702224
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 777.978940
## iter 10 value 95.792478
## iter 20 value 62.981142
## iter 30 value 41.625318
## iter 40 value 33.791814
## iter 50 value 27.615220
## iter 60 value 24.179139
## iter 70 value 22.920156
## iter 80 value 22.214663
## iter 90 value 21.816587
## iter 100 value 21.484591
## final value 21.484591
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1092.066483
## iter 10 value 107.706670
## iter 20 value 67.896112
## iter 30 value 44.808558
## iter 40 value 27.941255
## iter 50 value 23.079575
## iter 60 value 21.323320
## iter 70 value 20.136874
## iter 80 value 19.121237
## iter 90 value 17.471209
## iter 100 value 16.414872
## final value 16.414872
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 879.071871
## iter 10 value 98.187373
## iter 20 value 58.051081
## iter 30 value 32.287011
## iter 40 value 23.609610
## iter 50 value 21.183624
## iter 60 value 19.637979
## iter 70 value 18.295420
## iter 80 value 15.305260
## iter 90 value 13.826139
## iter 100 value 12.293083
## final value 12.293083
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 800.640702
## iter 10 value 100.957652
## iter 20 value 64.233301
## iter 30 value 43.824091
## iter 40 value 29.305842
## iter 50 value 20.084558
## iter 60 value 15.944624
## iter 70 value 14.734822
## iter 80 value 13.328702
## iter 90 value 12.524319
## iter 100 value 11.736975
## final value 11.736975
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1212.758021
## iter 10 value 83.706142
## iter 20 value 50.096657
## iter 30 value 28.584693
## iter 40 value 20.180075
## iter 50 value 17.634195
## iter 60 value 16.534883
## iter 70 value 15.773268
## iter 80 value 15.169253
## iter 90 value 14.708150
## iter 100 value 14.351008
## final value 14.351008
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 799.164846
## iter 10 value 118.048430
## iter 20 value 62.585574
## iter 30 value 44.265377
## iter 40 value 37.790556
## iter 50 value 32.863331
## iter 60 value 31.110209
## iter 70 value 27.806742
## iter 80 value 25.957823
## iter 90 value 23.031525
## iter 100 value 19.617477
## final value 19.617477
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 963.661425
## iter 10 value 93.681439
## iter 20 value 57.185191
## iter 30 value 30.405073
## iter 40 value 16.208599
## iter 50 value 10.482689
## iter 60 value 8.701844
## iter 70 value 7.369790
## iter 80 value 6.146871
## iter 90 value 4.919936
## iter 100 value 4.404099
## final value 4.404099
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 699.815071
## iter 10 value 93.434022
## iter 20 value 61.262798
## iter 30 value 36.712407
## iter 40 value 25.467183
## iter 50 value 21.507059
## iter 60 value 17.651906
## iter 70 value 16.281958
## iter 80 value 13.312904
## iter 90 value 9.563659
## iter 100 value 8.823126
## final value 8.823126
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 983.184909
## iter 10 value 133.089246
## iter 20 value 77.235414
## iter 30 value 50.462813
## iter 40 value 34.640529
## iter 50 value 28.954258
## iter 60 value 26.928382
## iter 70 value 25.402142
## iter 80 value 24.121357
## iter 90 value 23.005243
## iter 100 value 22.242163
## final value 22.242163
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 687.488189
## iter 10 value 115.761809
## iter 20 value 58.438169
## iter 30 value 39.198156
## iter 40 value 30.463869
## iter 50 value 23.509223
## iter 60 value 22.037725
## iter 70 value 20.931389
## iter 80 value 17.871657
## iter 90 value 17.634037
## iter 100 value 17.047310
## final value 17.047310
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 852.740527
## iter 10 value 296.826587
## iter 20 value 99.803074
## iter 30 value 65.714968
## iter 40 value 50.267101
## iter 50 value 41.072380
## iter 60 value 32.898226
## iter 70 value 29.975784
## iter 80 value 28.190156
## iter 90 value 26.931078
## iter 100 value 26.377665
## final value 26.377665
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1287.203902
## iter 10 value 148.239764
## iter 20 value 71.041989
## iter 30 value 43.533381
## iter 40 value 29.178093
## iter 50 value 19.842087
## iter 60 value 17.220469
## iter 70 value 16.060274
## iter 80 value 15.215011
## iter 90 value 14.763145
## iter 100 value 13.880500
## final value 13.880500
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 849.795501
## iter 10 value 127.723723
## iter 20 value 74.944405
## iter 30 value 55.718349
## iter 40 value 47.181400
## iter 50 value 43.505150
## iter 60 value 39.041062
## iter 70 value 34.535666
## iter 80 value 32.275601
## iter 90 value 29.790310
## iter 100 value 28.033459
## final value 28.033459
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 925.441927
## iter 10 value 186.257954
## iter 20 value 76.371313
## iter 30 value 46.826057
## iter 40 value 36.065142
## iter 50 value 29.226267
## iter 60 value 26.542728
## iter 70 value 22.735351
## iter 80 value 20.602296
## iter 90 value 19.297230
## iter 100 value 18.220449
## final value 18.220449
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 683.979654
## iter 10 value 132.692155
## iter 20 value 78.227511
## iter 30 value 57.057317
## iter 40 value 35.553948
## iter 50 value 22.765953
## iter 60 value 19.490841
## iter 70 value 17.258457
## iter 80 value 15.398829
## iter 90 value 14.009317
## iter 100 value 13.102336
## final value 13.102336
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 680.865806
## iter 10 value 97.459084
## iter 20 value 60.839101
## iter 30 value 38.657915
## iter 40 value 26.335660
## iter 50 value 18.174447
## iter 60 value 14.891251
## iter 70 value 12.428250
## iter 80 value 10.328584
## iter 90 value 7.344608
## iter 100 value 6.514914
## final value 6.514914
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1667.129691
## iter 10 value 113.667350
## iter 20 value 62.151396
## iter 30 value 45.125681
## iter 40 value 33.484727
## iter 50 value 26.769137
## iter 60 value 22.662861
## iter 70 value 20.985129
## iter 80 value 17.657403
## iter 90 value 17.022655
## iter 100 value 16.421650
## final value 16.421650
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1139.877512
## iter 10 value 177.808590
## iter 20 value 106.264487
## iter 30 value 75.811698
## iter 40 value 57.495985
## iter 50 value 49.622241
## iter 60 value 45.006322
## iter 70 value 40.738483
## iter 80 value 39.233750
## iter 90 value 38.757613
## iter 100 value 38.217642
## final value 38.217642
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 829.067857
## iter 10 value 103.891907
## iter 20 value 73.674911
## iter 30 value 54.344804
## iter 40 value 38.461989
## iter 50 value 28.775874
## iter 60 value 24.720098
## iter 70 value 19.854152
## iter 80 value 17.820989
## iter 90 value 17.063554
## iter 100 value 16.696932
## final value 16.696932
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 643.989652
## iter 10 value 80.143857
## iter 20 value 39.044513
## iter 30 value 21.616970
## iter 40 value 14.747692
## iter 50 value 12.368056
## iter 60 value 11.606387
## iter 70 value 11.098540
## iter 80 value 10.823768
## iter 90 value 10.645636
## iter 100 value 10.464049
## final value 10.464049
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1332.453300
## iter 10 value 112.087611
## iter 20 value 67.380567
## iter 30 value 46.612061
## iter 40 value 37.932712
## iter 50 value 33.286219
## iter 60 value 29.897148
## iter 70 value 26.905094
## iter 80 value 26.337871
## iter 90 value 25.679397
## iter 100 value 24.685034
## final value 24.685034
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 788.372160
## iter 10 value 104.917023
## iter 20 value 56.307939
## iter 30 value 31.444179
## iter 40 value 24.369388
## iter 50 value 22.632450
## iter 60 value 21.152850
## iter 70 value 20.197147
## iter 80 value 19.134670
## iter 90 value 17.555614
## iter 100 value 16.956952
## final value 16.956952
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 710.602199
## iter 10 value 136.316437
## iter 20 value 83.180613
## iter 30 value 51.507611
## iter 40 value 42.284182
## iter 50 value 38.627663
## iter 60 value 36.410689
## iter 70 value 35.695881
## iter 80 value 34.953172
## iter 90 value 33.774200
## iter 100 value 32.302541
## final value 32.302541
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 887.679857
## iter 10 value 189.808895
## iter 20 value 104.904571
## iter 30 value 88.102893
## iter 40 value 71.702766
## iter 50 value 66.537245
## iter 60 value 65.092314
## iter 70 value 63.768418
## iter 80 value 61.368705
## iter 90 value 58.164482
## iter 100 value 54.954261
## final value 54.954261
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 773.789370
## iter 10 value 118.791392
## iter 20 value 71.051083
## iter 30 value 38.869990
## iter 40 value 25.986375
## iter 50 value 22.630013
## iter 60 value 18.757743
## iter 70 value 17.446224
## iter 80 value 16.710960
## iter 90 value 15.909396
## iter 100 value 14.238387
## final value 14.238387
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 897.132731
## iter 10 value 114.811994
## iter 20 value 65.354129
## iter 30 value 46.853051
## iter 40 value 32.468544
## iter 50 value 27.523509
## iter 60 value 19.509632
## iter 70 value 14.758003
## iter 80 value 13.223858
## iter 90 value 12.440398
## iter 100 value 12.032153
## final value 12.032153
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1196.466479
## iter 10 value 159.623098
## iter 20 value 85.360089
## iter 30 value 65.259976
## iter 40 value 56.435430
## iter 50 value 48.818013
## iter 60 value 46.222199
## iter 70 value 43.662258
## iter 80 value 42.563220
## iter 90 value 40.195959
## iter 100 value 39.575602
## final value 39.575602
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 755.583024
## iter 10 value 103.566071
## iter 20 value 47.890997
## iter 30 value 34.736343
## iter 40 value 29.538059
## iter 50 value 24.343544
## iter 60 value 17.320209
## iter 70 value 10.741910
## iter 80 value 4.990671
## iter 90 value 4.052230
## iter 100 value 3.361908
## final value 3.361908
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1242.146158
## iter 10 value 147.077789
## iter 20 value 87.937031
## iter 30 value 62.389784
## iter 40 value 55.850401
## iter 50 value 48.947663
## iter 60 value 46.955147
## iter 70 value 45.635570
## iter 80 value 45.078158
## iter 90 value 44.749429
## iter 100 value 44.108247
## final value 44.108247
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 816.946900
## iter 10 value 132.719914
## iter 20 value 93.753648
## iter 30 value 62.549208
## iter 40 value 47.269924
## iter 50 value 35.764616
## iter 60 value 28.350165
## iter 70 value 25.281076
## iter 80 value 24.134161
## iter 90 value 23.505913
## iter 100 value 23.259790
## final value 23.259790
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 809.111164
## iter 10 value 103.750482
## iter 20 value 59.669390
## iter 30 value 37.501186
## iter 40 value 24.632648
## iter 50 value 19.575075
## iter 60 value 17.765568
## iter 70 value 16.189959
## iter 80 value 15.301991
## iter 90 value 14.280576
## iter 100 value 13.768064
## final value 13.768064
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 915.100045
## iter 10 value 109.420664
## iter 20 value 78.160582
## iter 30 value 53.249188
## iter 40 value 31.834899
## iter 50 value 23.351931
## iter 60 value 20.571855
## iter 70 value 18.637465
## iter 80 value 17.880235
## iter 90 value 17.220655
## iter 100 value 16.292866
## final value 16.292866
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1263.589125
## iter 10 value 139.609119
## iter 20 value 78.309996
## iter 30 value 47.013614
## iter 40 value 32.158451
## iter 50 value 28.546568
## iter 60 value 27.058221
## iter 70 value 25.859399
## iter 80 value 24.769940
## iter 90 value 24.264509
## iter 100 value 23.704748
## final value 23.704748
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 780.160385
## iter 10 value 123.893357
## iter 20 value 81.088638
## iter 30 value 58.601797
## iter 40 value 46.242922
## iter 50 value 32.030781
## iter 60 value 26.058899
## iter 70 value 24.038492
## iter 80 value 23.014877
## iter 90 value 22.331034
## iter 100 value 21.851062
## final value 21.851062
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1010.161550
## iter 10 value 105.501171
## iter 20 value 68.411743
## iter 30 value 52.067718
## iter 40 value 44.043857
## iter 50 value 41.575783
## iter 60 value 39.972392
## iter 70 value 38.239740
## iter 80 value 37.595807
## iter 90 value 37.088670
## iter 100 value 36.678034
## final value 36.678034
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1423.977386
## iter 10 value 155.540319
## iter 20 value 98.326193
## iter 30 value 75.401539
## iter 40 value 61.305124
## iter 50 value 53.669507
## iter 60 value 50.514213
## iter 70 value 47.625392
## iter 80 value 45.894591
## iter 90 value 45.080615
## iter 100 value 44.643840
## final value 44.643840
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1013.064979
## iter 10 value 116.829706
## iter 20 value 73.548321
## iter 30 value 41.247505
## iter 40 value 30.253929
## iter 50 value 26.641623
## iter 60 value 25.250118
## iter 70 value 24.658761
## iter 80 value 24.382511
## iter 90 value 24.154114
## iter 100 value 24.000522
## final value 24.000522
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 804.145628
## iter 10 value 111.635021
## iter 20 value 69.560968
## iter 30 value 56.643021
## iter 40 value 43.452440
## iter 50 value 35.830321
## iter 60 value 30.000858
## iter 70 value 28.613425
## iter 80 value 27.370687
## iter 90 value 26.274144
## iter 100 value 25.037251
## final value 25.037251
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1037.303229
## iter 10 value 103.614453
## iter 20 value 76.551572
## iter 30 value 57.895896
## iter 40 value 45.172995
## iter 50 value 39.366356
## iter 60 value 37.689122
## iter 70 value 32.816793
## iter 80 value 29.722755
## iter 90 value 25.995841
## iter 100 value 25.529268
## final value 25.529268
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1188.831695
## iter 10 value 101.003689
## iter 20 value 63.532525
## iter 30 value 44.044587
## iter 40 value 36.337645
## iter 50 value 33.081080
## iter 60 value 32.188175
## iter 70 value 30.541606
## iter 80 value 28.984703
## iter 90 value 28.060170
## iter 100 value 27.205607
## final value 27.205607
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 857.200102
## iter 10 value 160.921860
## iter 20 value 98.624700
## iter 30 value 74.093740
## iter 40 value 63.293483
## iter 50 value 55.864073
## iter 60 value 52.547161
## iter 70 value 51.056163
## iter 80 value 49.838374
## iter 90 value 48.770221
## iter 100 value 46.524894
## final value 46.524894
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 919.994278
## iter 10 value 122.066489
## iter 20 value 85.130452
## iter 30 value 65.683479
## iter 40 value 59.647371
## iter 50 value 56.277405
## iter 60 value 54.498693
## iter 70 value 53.473924
## iter 80 value 52.711660
## iter 90 value 51.752013
## iter 100 value 51.110364
## final value 51.110364
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1208.971388
## iter 10 value 126.677786
## iter 20 value 78.421113
## iter 30 value 56.584023
## iter 40 value 42.456945
## iter 50 value 37.820741
## iter 60 value 34.415467
## iter 70 value 31.469388
## iter 80 value 30.345868
## iter 90 value 28.340573
## iter 100 value 26.525890
## final value 26.525890
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 771.308609
## iter 10 value 95.963453
## iter 20 value 67.981418
## iter 30 value 43.778152
## iter 40 value 28.599267
## iter 50 value 20.214554
## iter 60 value 17.217222
## iter 70 value 15.705050
## iter 80 value 15.008070
## iter 90 value 14.431127
## iter 100 value 14.145820
## final value 14.145820
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 782.761442
## iter 10 value 71.319489
## iter 20 value 45.625115
## iter 30 value 34.495584
## iter 40 value 29.580593
## iter 50 value 26.605425
## iter 60 value 25.583203
## iter 70 value 24.440371
## iter 80 value 23.620390
## iter 90 value 18.831916
## iter 100 value 15.368152
## final value 15.368152
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1017.568640
## iter 10 value 110.452242
## iter 20 value 69.718227
## iter 30 value 45.070992
## iter 40 value 34.244479
## iter 50 value 24.871806
## iter 60 value 20.125160
## iter 70 value 16.858653
## iter 80 value 15.270539
## iter 90 value 13.936438
## iter 100 value 13.259083
## final value 13.259083
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1246.955438
## iter 10 value 112.240968
## iter 20 value 62.796690
## iter 30 value 44.363436
## iter 40 value 35.416877
## iter 50 value 30.047753
## iter 60 value 28.913682
## iter 70 value 24.587772
## iter 80 value 21.364140
## iter 90 value 20.456935
## iter 100 value 19.530966
## final value 19.530966
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1192.816508
## iter 10 value 130.633198
## iter 20 value 79.906372
## iter 30 value 47.241894
## iter 40 value 34.461385
## iter 50 value 29.639301
## iter 60 value 24.662214
## iter 70 value 23.407318
## iter 80 value 21.908416
## iter 90 value 21.367518
## iter 100 value 21.028300
## final value 21.028300
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 949.067110
## iter 10 value 126.029272
## iter 20 value 81.054366
## iter 30 value 61.133208
## iter 40 value 45.747999
## iter 50 value 38.851707
## iter 60 value 36.157090
## iter 70 value 31.693919
## iter 80 value 29.187784
## iter 90 value 25.203952
## iter 100 value 23.693202
## final value 23.693202
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 848.012931
## iter 10 value 143.597522
## iter 20 value 86.993909
## iter 30 value 69.181351
## iter 40 value 53.821300
## iter 50 value 47.817816
## iter 60 value 45.581099
## iter 70 value 44.352347
## iter 80 value 43.618756
## iter 90 value 41.376089
## iter 100 value 40.660504
## final value 40.660504
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 885.412130
## iter 10 value 146.403330
## iter 20 value 79.122463
## iter 30 value 55.973741
## iter 40 value 45.543977
## iter 50 value 41.216460
## iter 60 value 38.343041
## iter 70 value 36.568362
## iter 80 value 35.401842
## iter 90 value 30.939434
## iter 100 value 29.666269
## final value 29.666269
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1093.297446
## iter 10 value 117.568617
## iter 20 value 81.075880
## iter 30 value 55.404354
## iter 40 value 37.754665
## iter 50 value 31.106569
## iter 60 value 28.840061
## iter 70 value 25.133260
## iter 80 value 24.366423
## iter 90 value 23.509439
## iter 100 value 22.256744
## final value 22.256744
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1318.798358
## iter 10 value 166.405890
## iter 20 value 102.476628
## iter 30 value 76.560086
## iter 40 value 64.601760
## iter 50 value 58.580116
## iter 60 value 53.850770
## iter 70 value 50.536753
## iter 80 value 49.563107
## iter 90 value 49.030724
## iter 100 value 48.136654
## final value 48.136654
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1500.747041
## iter 10 value 200.088371
## iter 20 value 131.319984
## iter 30 value 109.971735
## iter 40 value 96.016060
## iter 50 value 85.202950
## iter 60 value 80.315830
## iter 70 value 77.026117
## iter 80 value 73.743879
## iter 90 value 72.205377
## iter 100 value 70.886998
## final value 70.886998
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 717.104478
## iter 10 value 103.201279
## iter 20 value 72.902223
## iter 30 value 53.418885
## iter 40 value 39.860123
## iter 50 value 36.419153
## iter 60 value 33.273905
## iter 70 value 30.837049
## iter 80 value 29.441095
## iter 90 value 28.527519
## iter 100 value 26.564929
## final value 26.564929
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1244.886725
## iter 10 value 170.333055
## iter 20 value 94.948213
## iter 30 value 63.773410
## iter 40 value 47.090742
## iter 50 value 38.820641
## iter 60 value 29.511717
## iter 70 value 24.622809
## iter 80 value 21.810226
## iter 90 value 20.080405
## iter 100 value 19.077397
## final value 19.077397
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1286.597773
## iter 10 value 113.506279
## iter 20 value 78.286334
## iter 30 value 49.970159
## iter 40 value 35.037983
## iter 50 value 31.266557
## iter 60 value 29.192103
## iter 70 value 25.279687
## iter 80 value 24.216055
## iter 90 value 23.447885
## iter 100 value 22.945966
## final value 22.945966
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 725.383705
## iter 10 value 112.106303
## iter 20 value 69.116422
## iter 30 value 45.001518
## iter 40 value 30.357346
## iter 50 value 24.204015
## iter 60 value 21.987986
## iter 70 value 21.018151
## iter 80 value 20.615562
## iter 90 value 20.170138
## iter 100 value 19.507214
## final value 19.507214
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 765.084571
## iter 10 value 188.326186
## iter 20 value 142.107220
## iter 30 value 99.004005
## iter 40 value 84.844191
## iter 50 value 65.772789
## iter 60 value 57.905967
## iter 70 value 52.091244
## iter 80 value 32.439282
## iter 90 value 29.067476
## iter 100 value 28.247721
## final value 28.247721
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 982.497231
## iter 10 value 150.112304
## iter 20 value 83.165710
## iter 30 value 56.954663
## iter 40 value 49.199927
## iter 50 value 45.916518
## iter 60 value 39.476337
## iter 70 value 37.376932
## iter 80 value 34.760932
## iter 90 value 32.589264
## iter 100 value 31.325665
## final value 31.325665
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1479.867580
## iter 10 value 105.145168
## iter 20 value 54.040771
## iter 30 value 34.931589
## iter 40 value 26.118300
## iter 50 value 23.681100
## iter 60 value 22.021751
## iter 70 value 20.255342
## iter 80 value 19.758659
## iter 90 value 19.342500
## iter 100 value 19.039874
## final value 19.039874
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 668.508121
## iter 10 value 109.734710
## iter 20 value 61.632419
## iter 30 value 35.064115
## iter 40 value 17.559829
## iter 50 value 13.833536
## iter 60 value 12.910993
## iter 70 value 12.162925
## iter 80 value 11.532633
## iter 90 value 10.957339
## iter 100 value 9.301939
## final value 9.301939
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 744.265384
## iter 10 value 96.534038
## iter 20 value 57.426260
## iter 30 value 25.739059
## iter 40 value 16.183234
## iter 50 value 10.561271
## iter 60 value 7.613261
## iter 70 value 7.120983
## iter 80 value 6.810812
## iter 90 value 6.552834
## iter 100 value 6.250758
## final value 6.250758
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 703.016738
## iter 10 value 112.362406
## iter 20 value 63.824293
## iter 30 value 37.348070
## iter 40 value 27.047140
## iter 50 value 22.595060
## iter 60 value 20.977434
## iter 70 value 19.448040
## iter 80 value 18.660430
## iter 90 value 18.097906
## iter 100 value 17.313405
## final value 17.313405
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1114.692918
## iter 10 value 159.044634
## iter 20 value 112.208157
## iter 30 value 83.748296
## iter 40 value 60.994315
## iter 50 value 51.992397
## iter 60 value 42.770745
## iter 70 value 37.724431
## iter 80 value 35.234266
## iter 90 value 33.743650
## iter 100 value 32.641034
## final value 32.641034
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1183.269900
## iter 10 value 129.233116
## iter 20 value 88.605290
## iter 30 value 71.626275
## iter 40 value 52.754534
## iter 50 value 40.337070
## iter 60 value 34.030073
## iter 70 value 30.980725
## iter 80 value 28.985506
## iter 90 value 27.985600
## iter 100 value 27.421625
## final value 27.421625
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 737.701005
## iter 10 value 105.804038
## iter 20 value 65.148890
## iter 30 value 34.076281
## iter 40 value 20.827170
## iter 50 value 15.700069
## iter 60 value 13.794605
## iter 70 value 12.656141
## iter 80 value 12.064852
## iter 90 value 11.662121
## iter 100 value 11.368178
## final value 11.368178
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 976.474187
## iter 10 value 113.682118
## iter 20 value 67.886362
## iter 30 value 42.198721
## iter 40 value 18.963673
## iter 50 value 14.589338
## iter 60 value 13.438620
## iter 70 value 12.387907
## iter 80 value 10.326221
## iter 90 value 9.600571
## iter 100 value 8.982548
## final value 8.982548
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 983.465786
## iter 10 value 128.544768
## iter 20 value 90.578919
## iter 30 value 72.634727
## iter 40 value 64.256021
## iter 50 value 61.179122
## iter 60 value 59.487543
## iter 70 value 56.612484
## iter 80 value 53.590193
## iter 90 value 48.465303
## iter 100 value 42.663990
## final value 42.663990
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 849.301257
## iter 10 value 165.215179
## iter 20 value 90.478704
## iter 30 value 73.117870
## iter 40 value 52.697440
## iter 50 value 42.720302
## iter 60 value 38.312908
## iter 70 value 36.524711
## iter 80 value 34.252288
## iter 90 value 32.710919
## iter 100 value 31.517049
## final value 31.517049
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1512.356852
## iter 10 value 136.065710
## iter 20 value 74.834238
## iter 30 value 50.690011
## iter 40 value 40.014837
## iter 50 value 31.013657
## iter 60 value 26.446622
## iter 70 value 21.900115
## iter 80 value 17.980143
## iter 90 value 17.119874
## iter 100 value 16.544588
## final value 16.544588
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1048.196149
## iter 10 value 192.195136
## iter 20 value 120.503895
## iter 30 value 93.408779
## iter 40 value 68.799954
## iter 50 value 55.033041
## iter 60 value 42.547455
## iter 70 value 39.748576
## iter 80 value 38.521534
## iter 90 value 37.470719
## iter 100 value 36.060325
## final value 36.060325
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1101.071740
## iter 10 value 103.445569
## iter 20 value 78.399751
## iter 30 value 56.342682
## iter 40 value 47.677678
## iter 50 value 43.595831
## iter 60 value 41.364156
## iter 70 value 39.682284
## iter 80 value 37.419985
## iter 90 value 35.573899
## iter 100 value 34.835620
## final value 34.835620
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1064.556334
## iter 10 value 115.938816
## iter 20 value 69.704934
## iter 30 value 46.678354
## iter 40 value 28.803827
## iter 50 value 23.849162
## iter 60 value 22.181944
## iter 70 value 20.956745
## iter 80 value 20.172900
## iter 90 value 19.638641
## iter 100 value 19.289013
## final value 19.289013
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 925.306912
## iter 10 value 101.736040
## iter 20 value 64.452195
## iter 30 value 40.263533
## iter 40 value 36.218000
## iter 50 value 34.710895
## iter 60 value 32.856643
## iter 70 value 28.147716
## iter 80 value 23.425921
## iter 90 value 21.696521
## iter 100 value 17.872144
## final value 17.872144
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1162.876607
## iter 10 value 123.422523
## iter 20 value 68.531967
## iter 30 value 45.576868
## iter 40 value 34.800270
## iter 50 value 29.560856
## iter 60 value 28.136098
## iter 70 value 26.398917
## iter 80 value 24.980866
## iter 90 value 23.596928
## iter 100 value 22.529622
## final value 22.529622
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 765.803802
## iter 10 value 111.993577
## iter 20 value 81.435175
## iter 30 value 58.857599
## iter 40 value 39.616649
## iter 50 value 35.199194
## iter 60 value 32.639846
## iter 70 value 30.569958
## iter 80 value 27.870095
## iter 90 value 25.855419
## iter 100 value 25.304513
## final value 25.304513
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1158.634808
## iter 10 value 151.018145
## iter 20 value 108.046731
## iter 30 value 84.408933
## iter 40 value 56.153036
## iter 50 value 44.442457
## iter 60 value 40.188897
## iter 70 value 38.535093
## iter 80 value 37.724010
## iter 90 value 36.793139
## iter 100 value 36.342216
## final value 36.342216
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 808.774416
## iter 10 value 198.836063
## iter 20 value 151.330966
## iter 30 value 130.286013
## iter 40 value 107.482308
## iter 50 value 97.065207
## iter 60 value 89.497204
## iter 70 value 83.352611
## iter 80 value 81.008444
## iter 90 value 79.071710
## iter 100 value 75.438289
## final value 75.438289
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 857.184367
## iter 10 value 144.232080
## iter 20 value 82.582562
## iter 30 value 60.769445
## iter 40 value 49.616094
## iter 50 value 40.825259
## iter 60 value 29.123932
## iter 70 value 23.448829
## iter 80 value 22.465470
## iter 90 value 19.632411
## iter 100 value 17.831211
## final value 17.831211
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1018.953052
## iter 10 value 149.711689
## iter 20 value 73.549547
## iter 30 value 42.697514
## iter 40 value 27.569470
## iter 50 value 19.334268
## iter 60 value 17.494577
## iter 70 value 16.167767
## iter 80 value 15.379304
## iter 90 value 14.587702
## iter 100 value 14.073665
## final value 14.073665
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 872.024822
## iter 10 value 113.559586
## iter 20 value 77.906263
## iter 30 value 47.000430
## iter 40 value 36.906265
## iter 50 value 30.624730
## iter 60 value 21.877159
## iter 70 value 17.926326
## iter 80 value 16.446260
## iter 90 value 15.578069
## iter 100 value 15.203897
## final value 15.203897
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 835.095584
## iter 10 value 89.795571
## iter 20 value 48.551516
## iter 30 value 23.841256
## iter 40 value 15.916861
## iter 50 value 13.784020
## iter 60 value 13.160443
## iter 70 value 12.679062
## iter 80 value 11.812056
## iter 90 value 11.205037
## iter 100 value 10.931020
## final value 10.931020
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 2614.876773
## iter 10 value 138.964774
## iter 20 value 78.227196
## iter 30 value 62.390707
## iter 40 value 57.025969
## iter 50 value 52.084813
## iter 60 value 43.035542
## iter 70 value 34.260752
## iter 80 value 30.778321
## iter 90 value 27.120343
## iter 100 value 25.576463
## final value 25.576463
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1855.226695
## iter 10 value 100.391261
## iter 20 value 67.171732
## iter 30 value 46.241575
## iter 40 value 25.992958
## iter 50 value 14.025467
## iter 60 value 11.247210
## iter 70 value 9.855698
## iter 80 value 6.890624
## iter 90 value 4.779428
## iter 100 value 4.152623
## final value 4.152623
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1313.302520
## iter 10 value 107.742531
## iter 20 value 47.214854
## iter 30 value 22.393633
## iter 40 value 15.363039
## iter 50 value 11.818790
## iter 60 value 10.222873
## iter 70 value 8.101742
## iter 80 value 7.173527
## iter 90 value 6.097089
## iter 100 value 4.040679
## final value 4.040679
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 643.596342
## iter 10 value 59.892902
## iter 20 value 29.638568
## iter 30 value 18.767665
## iter 40 value 16.574426
## iter 50 value 12.670981
## iter 60 value 10.782152
## iter 70 value 9.687050
## iter 80 value 7.964381
## iter 90 value 6.771767
## iter 100 value 5.582400
## final value 5.582400
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1001.718527
## iter 10 value 80.291342
## iter 20 value 30.323977
## iter 30 value 15.684674
## iter 40 value 8.582161
## iter 50 value 4.197978
## iter 60 value 2.869367
## iter 70 value 2.503065
## iter 80 value 2.323130
## iter 90 value 2.177287
## iter 100 value 2.116280
## final value 2.116280
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 669.774370
## iter 10 value 92.796639
## iter 20 value 51.036084
## iter 30 value 25.716715
## iter 40 value 10.573169
## iter 50 value 8.915984
## iter 60 value 8.012317
## iter 70 value 7.292117
## iter 80 value 5.834378
## iter 90 value 4.780609
## iter 100 value 4.354950
## final value 4.354950
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1316.056144
## iter 10 value 103.578697
## iter 20 value 57.143659
## iter 30 value 36.493346
## iter 40 value 19.503089
## iter 50 value 9.791526
## iter 60 value 5.539887
## iter 70 value 4.627889
## iter 80 value 4.073573
## iter 90 value 3.749367
## iter 100 value 3.439983
## final value 3.439983
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 618.637360
## iter 10 value 111.428958
## iter 20 value 72.520777
## iter 30 value 55.135757
## iter 40 value 37.001927
## iter 50 value 28.687970
## iter 60 value 25.560606
## iter 70 value 22.428044
## iter 80 value 20.551782
## iter 90 value 20.128692
## iter 100 value 19.450989
## final value 19.450989
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1128.242302
## iter 10 value 129.348608
## iter 20 value 72.123020
## iter 30 value 43.467332
## iter 40 value 33.139753
## iter 50 value 30.556544
## iter 60 value 28.491253
## iter 70 value 27.095830
## iter 80 value 26.091274
## iter 90 value 24.407783
## iter 100 value 23.078902
## final value 23.078902
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 681.211823
## iter 10 value 112.266915
## iter 20 value 68.579285
## iter 30 value 40.493788
## iter 40 value 29.473391
## iter 50 value 26.092129
## iter 60 value 23.976540
## iter 70 value 23.305581
## iter 80 value 22.758147
## iter 90 value 21.114436
## iter 100 value 19.084113
## final value 19.084113
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 925.434907
## iter 10 value 120.798259
## iter 20 value 76.306749
## iter 30 value 56.640446
## iter 40 value 38.648802
## iter 50 value 24.390034
## iter 60 value 18.871170
## iter 70 value 17.125397
## iter 80 value 15.512121
## iter 90 value 14.706719
## iter 100 value 14.267173
## final value 14.267173
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 843.330138
## iter 10 value 101.125820
## iter 20 value 57.687282
## iter 30 value 29.103583
## iter 40 value 15.897452
## iter 50 value 10.806845
## iter 60 value 9.427413
## iter 70 value 8.342497
## iter 80 value 7.700418
## iter 90 value 7.415874
## iter 100 value 6.975170
## final value 6.975170
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1310.497267
## iter 10 value 149.567787
## iter 20 value 106.781737
## iter 30 value 74.173792
## iter 40 value 48.585000
## iter 50 value 35.245807
## iter 60 value 29.909005
## iter 70 value 28.492837
## iter 80 value 27.411981
## iter 90 value 25.588086
## iter 100 value 24.183064
## final value 24.183064
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1801.742438
## iter 10 value 107.196249
## iter 20 value 54.698633
## iter 30 value 37.826937
## iter 40 value 30.613120
## iter 50 value 26.548262
## iter 60 value 25.298246
## iter 70 value 22.671279
## iter 80 value 21.470550
## iter 90 value 20.743566
## iter 100 value 19.422518
## final value 19.422518
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 794.965726
## iter 10 value 97.159146
## iter 20 value 67.584150
## iter 30 value 50.544310
## iter 40 value 40.084395
## iter 50 value 34.040450
## iter 60 value 31.579092
## iter 70 value 28.578526
## iter 80 value 27.217417
## iter 90 value 26.204830
## iter 100 value 25.317298
## final value 25.317298
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 989.808921
## iter 10 value 113.382809
## iter 20 value 81.856531
## iter 30 value 60.752972
## iter 40 value 39.587186
## iter 50 value 31.551049
## iter 60 value 26.598254
## iter 70 value 23.966387
## iter 80 value 20.526108
## iter 90 value 18.005547
## iter 100 value 16.634897
## final value 16.634897
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1244.726624
## iter 10 value 90.674495
## iter 20 value 57.160397
## iter 30 value 31.337304
## iter 40 value 12.763744
## iter 50 value 9.615036
## iter 60 value 8.611587
## iter 70 value 7.922884
## iter 80 value 7.290200
## iter 90 value 6.591097
## iter 100 value 6.129707
## final value 6.129707
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1125.363113
## iter 10 value 117.424650
## iter 20 value 60.072800
## iter 30 value 45.752645
## iter 40 value 39.214014
## iter 50 value 34.784311
## iter 60 value 32.131514
## iter 70 value 30.979897
## iter 80 value 30.469863
## iter 90 value 30.274551
## iter 100 value 30.135174
## final value 30.135174
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1308.227587
## iter 10 value 193.075276
## iter 20 value 92.410569
## iter 30 value 72.411616
## iter 40 value 61.941417
## iter 50 value 56.260171
## iter 60 value 49.467481
## iter 70 value 43.080182
## iter 80 value 38.683816
## iter 90 value 34.999633
## iter 100 value 32.344093
## final value 32.344093
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 891.159956
## iter 10 value 110.274519
## iter 20 value 60.429573
## iter 30 value 37.488406
## iter 40 value 27.503841
## iter 50 value 24.056729
## iter 60 value 22.755008
## iter 70 value 21.867420
## iter 80 value 21.361398
## iter 90 value 20.961775
## iter 100 value 20.207347
## final value 20.207347
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 902.161358
## iter 10 value 99.372244
## iter 20 value 67.119334
## iter 30 value 43.926271
## iter 40 value 21.211144
## iter 50 value 11.018170
## iter 60 value 6.635193
## iter 70 value 4.773271
## iter 80 value 4.293777
## iter 90 value 3.976633
## iter 100 value 3.831670
## final value 3.831670
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1139.478240
## iter 10 value 115.457873
## iter 20 value 65.293327
## iter 30 value 52.839759
## iter 40 value 45.835393
## iter 50 value 44.044262
## iter 60 value 43.173404
## iter 70 value 42.863009
## iter 80 value 42.595372
## iter 90 value 42.247898
## iter 100 value 41.659744
## final value 41.659744
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1062.102952
## iter 10 value 115.416535
## iter 20 value 71.606266
## iter 30 value 54.618273
## iter 40 value 42.703696
## iter 50 value 38.137243
## iter 60 value 35.525959
## iter 70 value 30.123082
## iter 80 value 28.718066
## iter 90 value 27.793050
## iter 100 value 25.492885
## final value 25.492885
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 651.273220
## iter 10 value 106.288936
## iter 20 value 64.216211
## iter 30 value 38.486919
## iter 40 value 29.232129
## iter 50 value 24.913136
## iter 60 value 23.467228
## iter 70 value 19.949422
## iter 80 value 17.586955
## iter 90 value 14.163508
## iter 100 value 13.758380
## final value 13.758380
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1411.066133
## iter 10 value 200.362939
## iter 20 value 114.073358
## iter 30 value 85.076801
## iter 40 value 77.737922
## iter 50 value 73.157864
## iter 60 value 69.973968
## iter 70 value 66.031225
## iter 80 value 63.013628
## iter 90 value 61.498719
## iter 100 value 59.063846
## final value 59.063846
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 941.452427
## iter 10 value 94.305486
## iter 20 value 57.195288
## iter 30 value 32.682803
## iter 40 value 21.518048
## iter 50 value 16.691414
## iter 60 value 13.901296
## iter 70 value 11.922324
## iter 80 value 10.985429
## iter 90 value 9.829397
## iter 100 value 9.308620
## final value 9.308620
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 928.920421
## iter 10 value 91.552236
## iter 20 value 53.002575
## iter 30 value 42.125898
## iter 40 value 28.474366
## iter 50 value 22.796701
## iter 60 value 20.679953
## iter 70 value 19.322888
## iter 80 value 17.192965
## iter 90 value 16.320444
## iter 100 value 15.862065
## final value 15.862065
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 694.380381
## iter 10 value 107.934136
## iter 20 value 64.928846
## iter 30 value 38.526495
## iter 40 value 27.420224
## iter 50 value 22.289606
## iter 60 value 21.174856
## iter 70 value 20.565645
## iter 80 value 20.240967
## iter 90 value 20.010163
## iter 100 value 19.528615
## final value 19.528615
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 799.215600
## iter 10 value 97.644550
## iter 20 value 70.386962
## iter 30 value 43.401183
## iter 40 value 32.642779
## iter 50 value 21.256059
## iter 60 value 19.097378
## iter 70 value 18.344391
## iter 80 value 15.799393
## iter 90 value 15.228753
## iter 100 value 14.959321
## final value 14.959321
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 705.963160
## iter 10 value 82.062447
## iter 20 value 33.213463
## iter 30 value 17.082182
## iter 40 value 6.396644
## iter 50 value 3.472983
## iter 60 value 3.004936
## iter 70 value 2.680889
## iter 80 value 2.472310
## iter 90 value 2.248716
## iter 100 value 2.083130
## final value 2.083130
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1202.251429
## iter 10 value 141.771463
## iter 20 value 75.894557
## iter 30 value 39.799039
## iter 40 value 24.371888
## iter 50 value 20.564786
## iter 60 value 19.465189
## iter 70 value 18.677623
## iter 80 value 17.263778
## iter 90 value 16.343946
## iter 100 value 16.127547
## final value 16.127547
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 822.258873
## iter 10 value 137.616503
## iter 20 value 87.111844
## iter 30 value 71.933603
## iter 40 value 66.606920
## iter 50 value 62.380021
## iter 60 value 57.925791
## iter 70 value 54.341291
## iter 80 value 50.853712
## iter 90 value 49.218597
## iter 100 value 47.759980
## final value 47.759980
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1139.099627
## iter 10 value 102.346436
## iter 20 value 61.616992
## iter 30 value 41.404340
## iter 40 value 29.082847
## iter 50 value 23.868184
## iter 60 value 21.676986
## iter 70 value 20.507540
## iter 80 value 19.186456
## iter 90 value 18.046796
## iter 100 value 16.594898
## final value 16.594898
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1440.268784
## iter 10 value 94.566568
## iter 20 value 55.902022
## iter 30 value 39.603767
## iter 40 value 28.168363
## iter 50 value 21.849157
## iter 60 value 19.455834
## iter 70 value 18.045985
## iter 80 value 17.066342
## iter 90 value 16.446887
## iter 100 value 14.867254
## final value 14.867254
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 868.589682
## iter 10 value 87.780184
## iter 20 value 40.629760
## iter 30 value 23.462170
## iter 40 value 15.735315
## iter 50 value 11.493857
## iter 60 value 9.713263
## iter 70 value 9.242671
## iter 80 value 8.894662
## iter 90 value 8.088157
## iter 100 value 7.848878
## final value 7.848878
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1031.924821
## iter 10 value 122.178696
## iter 20 value 72.819238
## iter 30 value 47.561189
## iter 40 value 36.582186
## iter 50 value 33.354319
## iter 60 value 31.637162
## iter 70 value 31.167352
## iter 80 value 30.548677
## iter 90 value 30.004200
## iter 100 value 29.593224
## final value 29.593224
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1173.862952
## iter 10 value 126.724493
## iter 20 value 67.501069
## iter 30 value 40.889229
## iter 40 value 25.597680
## iter 50 value 19.557472
## iter 60 value 17.688795
## iter 70 value 17.203390
## iter 80 value 16.665420
## iter 90 value 16.178475
## iter 100 value 15.527547
## final value 15.527547
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 707.115956
## iter 10 value 116.501945
## iter 20 value 80.410384
## iter 30 value 55.005623
## iter 40 value 43.966127
## iter 50 value 39.298840
## iter 60 value 37.032820
## iter 70 value 34.905550
## iter 80 value 33.766464
## iter 90 value 32.740680
## iter 100 value 31.644463
## final value 31.644463
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 804.384052
## iter 10 value 117.502929
## iter 20 value 77.619549
## iter 30 value 50.567518
## iter 40 value 39.161274
## iter 50 value 34.674056
## iter 60 value 30.937981
## iter 70 value 29.144615
## iter 80 value 28.312308
## iter 90 value 27.256064
## iter 100 value 26.583606
## final value 26.583606
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 758.271739
## iter 10 value 161.609010
## iter 20 value 96.034719
## iter 30 value 75.916287
## iter 40 value 60.402852
## iter 50 value 46.854564
## iter 60 value 38.933783
## iter 70 value 34.735679
## iter 80 value 32.251051
## iter 90 value 28.694373
## iter 100 value 26.093671
## final value 26.093671
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 829.962015
## iter 10 value 108.233320
## iter 20 value 58.811588
## iter 30 value 38.144498
## iter 40 value 33.145312
## iter 50 value 31.388261
## iter 60 value 30.587026
## iter 70 value 30.299924
## iter 80 value 30.070575
## iter 90 value 29.934667
## iter 100 value 29.631911
## final value 29.631911
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1127.044586
## iter 10 value 104.854159
## iter 20 value 67.204683
## iter 30 value 38.707627
## iter 40 value 20.538750
## iter 50 value 16.685461
## iter 60 value 15.549278
## iter 70 value 14.444839
## iter 80 value 13.795980
## iter 90 value 13.389885
## iter 100 value 13.156907
## final value 13.156907
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 872.176538
## iter 10 value 157.414755
## iter 20 value 100.058603
## iter 30 value 76.025051
## iter 40 value 42.364054
## iter 50 value 29.742492
## iter 60 value 25.728987
## iter 70 value 24.381717
## iter 80 value 23.617921
## iter 90 value 23.000039
## iter 100 value 22.612791
## final value 22.612791
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 977.729314
## iter 10 value 132.593862
## iter 20 value 92.838236
## iter 30 value 71.410770
## iter 40 value 58.249016
## iter 50 value 43.239355
## iter 60 value 37.831266
## iter 70 value 36.240067
## iter 80 value 34.296015
## iter 90 value 32.859008
## iter 100 value 31.923694
## final value 31.923694
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1332.000452
## iter 10 value 107.468222
## iter 20 value 78.354344
## iter 30 value 49.481774
## iter 40 value 32.660105
## iter 50 value 23.622079
## iter 60 value 21.479627
## iter 70 value 20.072227
## iter 80 value 19.076455
## iter 90 value 18.615434
## iter 100 value 17.502519
## final value 17.502519
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1777.552779
## iter 10 value 127.948556
## iter 20 value 75.979727
## iter 30 value 56.861721
## iter 40 value 51.494227
## iter 50 value 48.658596
## iter 60 value 46.128190
## iter 70 value 44.778716
## iter 80 value 43.952390
## iter 90 value 43.444735
## iter 100 value 42.707583
## final value 42.707583
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 721.405272
## iter 10 value 159.617116
## iter 20 value 119.440638
## iter 30 value 102.316501
## iter 40 value 88.669028
## iter 50 value 77.492241
## iter 60 value 67.142025
## iter 70 value 59.816117
## iter 80 value 55.006506
## iter 90 value 51.986744
## iter 100 value 48.809707
## final value 48.809707
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 881.003257
## iter 10 value 149.943144
## iter 20 value 99.192326
## iter 30 value 62.317486
## iter 40 value 44.137504
## iter 50 value 36.531300
## iter 60 value 33.595170
## iter 70 value 32.617208
## iter 80 value 31.971963
## iter 90 value 31.641724
## iter 100 value 31.429740
## final value 31.429740
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1490.216566
## iter 10 value 131.936911
## iter 20 value 88.077524
## iter 30 value 70.811378
## iter 40 value 57.288783
## iter 50 value 49.843103
## iter 60 value 44.764321
## iter 70 value 41.814265
## iter 80 value 40.718875
## iter 90 value 40.008021
## iter 100 value 39.005373
## final value 39.005373
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 602.977024
## iter 10 value 81.916936
## iter 20 value 36.543117
## iter 30 value 14.093603
## iter 40 value 6.712179
## iter 50 value 5.977936
## iter 60 value 5.257628
## iter 70 value 4.455097
## iter 80 value 4.072794
## iter 90 value 3.949209
## iter 100 value 3.874716
## final value 3.874716
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 905.459689
## iter 10 value 87.775260
## iter 20 value 60.488821
## iter 30 value 39.967229
## iter 40 value 19.278600
## iter 50 value 15.364087
## iter 60 value 13.447160
## iter 70 value 12.251456
## iter 80 value 11.704857
## iter 90 value 11.192459
## iter 100 value 8.844190
## final value 8.844190
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1538.129255
## iter 10 value 244.909542
## iter 20 value 102.768325
## iter 30 value 68.507492
## iter 40 value 53.481096
## iter 50 value 45.260513
## iter 60 value 42.727321
## iter 70 value 41.885032
## iter 80 value 40.276497
## iter 90 value 39.248130
## iter 100 value 38.250390
## final value 38.250390
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 876.765806
## iter 10 value 102.243778
## iter 20 value 64.412121
## iter 30 value 33.012252
## iter 40 value 26.027412
## iter 50 value 23.179156
## iter 60 value 22.230955
## iter 70 value 21.745846
## iter 80 value 21.269525
## iter 90 value 20.974142
## iter 100 value 20.241918
## final value 20.241918
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1229.268593
## iter 10 value 235.769938
## iter 20 value 96.359450
## iter 30 value 54.534489
## iter 40 value 37.393512
## iter 50 value 28.346223
## iter 60 value 25.108182
## iter 70 value 21.645736
## iter 80 value 20.176711
## iter 90 value 18.175460
## iter 100 value 15.305250
## final value 15.305250
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1373.947776
## iter 10 value 116.118955
## iter 20 value 74.737077
## iter 30 value 55.286402
## iter 40 value 46.870614
## iter 50 value 40.350580
## iter 60 value 37.917823
## iter 70 value 36.022731
## iter 80 value 35.029902
## iter 90 value 33.693737
## iter 100 value 32.905702
## final value 32.905702
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 958.467477
## iter 10 value 106.815645
## iter 20 value 78.142167
## iter 30 value 59.075297
## iter 40 value 43.748628
## iter 50 value 39.528607
## iter 60 value 37.225546
## iter 70 value 35.098707
## iter 80 value 33.870376
## iter 90 value 32.758473
## iter 100 value 32.030941
## final value 32.030941
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1020.085041
## iter 10 value 120.312581
## iter 20 value 75.490103
## iter 30 value 50.262916
## iter 40 value 32.672800
## iter 50 value 26.408982
## iter 60 value 24.998748
## iter 70 value 24.578554
## iter 80 value 24.210696
## iter 90 value 24.041079
## iter 100 value 23.963108
## final value 23.963108
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 787.670431
## iter 10 value 99.626049
## iter 20 value 55.483483
## iter 30 value 38.777870
## iter 40 value 26.144862
## iter 50 value 17.342496
## iter 60 value 12.526539
## iter 70 value 11.222591
## iter 80 value 10.371436
## iter 90 value 9.383350
## iter 100 value 9.086345
## final value 9.086345
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 816.459415
## iter 10 value 128.363198
## iter 20 value 67.422538
## iter 30 value 43.336691
## iter 40 value 35.238251
## iter 50 value 29.203144
## iter 60 value 25.703217
## iter 70 value 22.351467
## iter 80 value 19.453724
## iter 90 value 17.275650
## iter 100 value 13.566179
## final value 13.566179
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 669.572003
## iter 10 value 95.439608
## iter 20 value 57.058007
## iter 30 value 37.675860
## iter 40 value 31.615621
## iter 50 value 29.183409
## iter 60 value 28.490178
## iter 70 value 27.930956
## iter 80 value 27.627639
## iter 90 value 27.405988
## iter 100 value 27.092092
## final value 27.092092
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 937.947896
## iter 10 value 155.433146
## iter 20 value 93.332267
## iter 30 value 69.692317
## iter 40 value 51.874773
## iter 50 value 44.911820
## iter 60 value 42.371358
## iter 70 value 40.751692
## iter 80 value 39.730398
## iter 90 value 38.931500
## iter 100 value 37.901559
## final value 37.901559
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1362.681245
## iter 10 value 136.645098
## iter 20 value 70.398410
## iter 30 value 49.035655
## iter 40 value 36.478044
## iter 50 value 27.411734
## iter 60 value 22.805904
## iter 70 value 21.659677
## iter 80 value 21.026508
## iter 90 value 20.417045
## iter 100 value 19.855364
## final value 19.855364
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 801.712184
## iter 10 value 106.890440
## iter 20 value 63.715239
## iter 30 value 42.284589
## iter 40 value 26.934272
## iter 50 value 21.469420
## iter 60 value 20.583393
## iter 70 value 20.259205
## iter 80 value 19.760058
## iter 90 value 19.437702
## iter 100 value 19.080187
## final value 19.080187
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 958.893356
## iter 10 value 117.739912
## iter 20 value 78.514909
## iter 30 value 55.458553
## iter 40 value 33.971222
## iter 50 value 19.004447
## iter 60 value 13.639367
## iter 70 value 11.556993
## iter 80 value 10.190688
## iter 90 value 9.234001
## iter 100 value 8.671773
## final value 8.671773
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 684.924668
## iter 10 value 123.810830
## iter 20 value 72.784528
## iter 30 value 44.544528
## iter 40 value 31.141320
## iter 50 value 24.483270
## iter 60 value 18.919017
## iter 70 value 16.427357
## iter 80 value 14.991818
## iter 90 value 14.601774
## iter 100 value 13.428682
## final value 13.428682
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 752.596001
## iter 10 value 119.070549
## iter 20 value 74.312208
## iter 30 value 47.310515
## iter 40 value 26.828916
## iter 50 value 15.387029
## iter 60 value 12.609255
## iter 70 value 11.196926
## iter 80 value 10.437621
## iter 90 value 9.919474
## iter 100 value 9.611893
## final value 9.611893
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1178.233716
## iter 10 value 135.212980
## iter 20 value 86.382072
## iter 30 value 65.024156
## iter 40 value 53.408779
## iter 50 value 46.324939
## iter 60 value 40.677681
## iter 70 value 37.644692
## iter 80 value 35.456070
## iter 90 value 32.397750
## iter 100 value 30.913243
## final value 30.913243
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1171.507442
## iter 10 value 112.400226
## iter 20 value 77.380539
## iter 30 value 54.309499
## iter 40 value 35.359586
## iter 50 value 29.231590
## iter 60 value 27.607850
## iter 70 value 24.954465
## iter 80 value 24.042056
## iter 90 value 23.278657
## iter 100 value 22.716072
## final value 22.716072
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 986.382778
## iter 10 value 124.118390
## iter 20 value 84.980406
## iter 30 value 59.662226
## iter 40 value 48.789330
## iter 50 value 41.205694
## iter 60 value 38.055544
## iter 70 value 35.583952
## iter 80 value 34.255801
## iter 90 value 33.545982
## iter 100 value 31.454750
## final value 31.454750
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1035.654600
## iter 10 value 136.872998
## iter 20 value 89.670162
## iter 30 value 69.339049
## iter 40 value 60.014844
## iter 50 value 55.864084
## iter 60 value 53.918867
## iter 70 value 52.268309
## iter 80 value 50.519994
## iter 90 value 49.972612
## iter 100 value 49.299920
## final value 49.299920
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 998.883779
## iter 10 value 122.692282
## iter 20 value 75.875781
## iter 30 value 40.014843
## iter 40 value 25.618163
## iter 50 value 20.952140
## iter 60 value 19.818228
## iter 70 value 18.712546
## iter 80 value 18.155922
## iter 90 value 17.779288
## iter 100 value 17.506133
## final value 17.506133
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 870.909606
## iter 10 value 126.546157
## iter 20 value 86.308122
## iter 30 value 64.365120
## iter 40 value 46.706123
## iter 50 value 36.964914
## iter 60 value 34.306044
## iter 70 value 32.338484
## iter 80 value 30.550328
## iter 90 value 29.727218
## iter 100 value 29.062878
## final value 29.062878
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1094.977614
## iter 10 value 246.996597
## iter 20 value 138.386626
## iter 30 value 110.186737
## iter 40 value 93.030385
## iter 50 value 81.882121
## iter 60 value 73.548204
## iter 70 value 66.941985
## iter 80 value 64.128388
## iter 90 value 61.621035
## iter 100 value 59.681211
## final value 59.681211
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1343.003996
## iter 10 value 105.950090
## iter 20 value 60.544584
## iter 30 value 38.174752
## iter 40 value 23.964806
## iter 50 value 15.568825
## iter 60 value 12.843077
## iter 70 value 11.031880
## iter 80 value 10.425515
## iter 90 value 9.984542
## iter 100 value 9.187526
## final value 9.187526
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 989.188011
## iter 10 value 125.778133
## iter 20 value 66.108669
## iter 30 value 43.936663
## iter 40 value 31.406872
## iter 50 value 28.493088
## iter 60 value 24.026612
## iter 70 value 21.834237
## iter 80 value 20.165868
## iter 90 value 19.666848
## iter 100 value 19.156032
## final value 19.156032
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 660.795265
## iter 10 value 128.244688
## iter 20 value 82.591528
## iter 30 value 54.438843
## iter 40 value 32.519308
## iter 50 value 24.797713
## iter 60 value 23.056237
## iter 70 value 22.185000
## iter 80 value 21.700963
## iter 90 value 20.903415
## iter 100 value 18.680382
## final value 18.680382
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1377.816859
## iter 10 value 98.815110
## iter 20 value 50.206089
## iter 30 value 26.937219
## iter 40 value 17.035395
## iter 50 value 15.216679
## iter 60 value 13.968427
## iter 70 value 12.923854
## iter 80 value 12.150796
## iter 90 value 10.426617
## iter 100 value 9.776004
## final value 9.776004
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 685.616329
## iter 10 value 96.856272
## iter 20 value 68.690326
## iter 30 value 37.684639
## iter 40 value 21.699825
## iter 50 value 18.122961
## iter 60 value 17.106474
## iter 70 value 16.393987
## iter 80 value 16.168592
## iter 90 value 15.991195
## iter 100 value 15.872940
## final value 15.872940
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 2030.096629
## iter 10 value 181.628074
## iter 20 value 94.709359
## iter 30 value 68.238904
## iter 40 value 50.700848
## iter 50 value 38.409176
## iter 60 value 32.264590
## iter 70 value 29.378007
## iter 80 value 27.795762
## iter 90 value 27.016899
## iter 100 value 25.701299
## final value 25.701299
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 794.231695
## iter 10 value 231.062594
## iter 20 value 122.550187
## iter 30 value 88.163432
## iter 40 value 65.920711
## iter 50 value 56.638999
## iter 60 value 50.889699
## iter 70 value 44.771752
## iter 80 value 42.624642
## iter 90 value 40.978539
## iter 100 value 35.273685
## final value 35.273685
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 804.352282
## iter 10 value 92.328850
## iter 20 value 54.440550
## iter 30 value 33.696359
## iter 40 value 26.385102
## iter 50 value 20.006807
## iter 60 value 17.215250
## iter 70 value 15.624967
## iter 80 value 14.230992
## iter 90 value 12.402213
## iter 100 value 11.422207
## final value 11.422207
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 791.922526
## iter 10 value 80.374726
## iter 20 value 45.850687
## iter 30 value 26.085471
## iter 40 value 20.313954
## iter 50 value 18.705951
## iter 60 value 17.292206
## iter 70 value 16.321032
## iter 80 value 15.723372
## iter 90 value 15.330824
## iter 100 value 14.697972
## final value 14.697972
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1142.634006
## iter 10 value 91.953014
## iter 20 value 57.735639
## iter 30 value 47.014894
## iter 40 value 34.769464
## iter 50 value 28.140591
## iter 60 value 20.303592
## iter 70 value 14.611589
## iter 80 value 13.746811
## iter 90 value 13.160948
## iter 100 value 12.596326
## final value 12.596326
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 917.610518
## iter 10 value 73.619242
## iter 20 value 42.993174
## iter 30 value 16.429382
## iter 40 value 10.205193
## iter 50 value 8.542214
## iter 60 value 7.570815
## iter 70 value 6.468226
## iter 80 value 4.403770
## iter 90 value 3.446107
## iter 100 value 2.936384
## final value 2.936384
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1453.258778
## iter 10 value 131.386731
## iter 20 value 81.209128
## iter 30 value 54.783246
## iter 40 value 49.362275
## iter 50 value 45.367482
## iter 60 value 43.559367
## iter 70 value 41.792054
## iter 80 value 40.515015
## iter 90 value 38.619932
## iter 100 value 37.717058
## final value 37.717058
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1004.394543
## iter 10 value 78.686051
## iter 20 value 40.177672
## iter 30 value 23.011903
## iter 40 value 18.559099
## iter 50 value 17.403519
## iter 60 value 16.899541
## iter 70 value 16.365314
## iter 80 value 11.306719
## iter 90 value 9.410705
## iter 100 value 7.292516
## final value 7.292516
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 702.734526
## iter 10 value 92.902451
## iter 20 value 61.293274
## iter 30 value 35.873042
## iter 40 value 27.889186
## iter 50 value 25.995341
## iter 60 value 19.966632
## iter 70 value 15.425380
## iter 80 value 13.849271
## iter 90 value 12.695455
## iter 100 value 11.947783
## final value 11.947783
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1009.405591
## iter 10 value 97.152003
## iter 20 value 59.597840
## iter 30 value 30.529994
## iter 40 value 24.966191
## iter 50 value 23.095315
## iter 60 value 22.247320
## iter 70 value 21.929189
## iter 80 value 20.309442
## iter 90 value 19.648262
## iter 100 value 17.842185
## final value 17.842185
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 970.097635
## iter 10 value 103.346826
## iter 20 value 53.905040
## iter 30 value 34.485881
## iter 40 value 28.085847
## iter 50 value 26.603093
## iter 60 value 25.824382
## iter 70 value 24.957690
## iter 80 value 24.590607
## iter 90 value 21.856082
## iter 100 value 20.611685
## final value 20.611685
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 794.816883
## iter 10 value 241.215163
## iter 20 value 163.696829
## iter 30 value 131.307213
## iter 40 value 93.707622
## iter 50 value 69.477284
## iter 60 value 60.459378
## iter 70 value 54.115491
## iter 80 value 45.898151
## iter 90 value 36.691753
## iter 100 value 32.984269
## final value 32.984269
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1346.833580
## iter 10 value 128.774481
## iter 20 value 84.764042
## iter 30 value 71.453646
## iter 40 value 53.230890
## iter 50 value 36.693667
## iter 60 value 31.452043
## iter 70 value 28.250114
## iter 80 value 26.767901
## iter 90 value 25.914109
## iter 100 value 25.215486
## final value 25.215486
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1115.475846
## iter 10 value 98.822667
## iter 20 value 51.664292
## iter 30 value 35.632678
## iter 40 value 28.008646
## iter 50 value 25.978566
## iter 60 value 23.793653
## iter 70 value 22.387126
## iter 80 value 21.201132
## iter 90 value 20.279423
## iter 100 value 19.692820
## final value 19.692820
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 994.473465
## iter 10 value 112.782070
## iter 20 value 81.537322
## iter 30 value 70.462529
## iter 40 value 64.043629
## iter 50 value 61.237440
## iter 60 value 59.409713
## iter 70 value 57.632632
## iter 80 value 56.859597
## iter 90 value 55.616237
## iter 100 value 51.534178
## final value 51.534178
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1076.077352
## iter 10 value 115.214574
## iter 20 value 59.734285
## iter 30 value 33.030560
## iter 40 value 21.204970
## iter 50 value 19.952812
## iter 60 value 17.996608
## iter 70 value 17.169366
## iter 80 value 16.667525
## iter 90 value 16.216140
## iter 100 value 15.799407
## final value 15.799407
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 999.981494
## iter 10 value 108.303090
## iter 20 value 71.324767
## iter 30 value 44.401046
## iter 40 value 36.433766
## iter 50 value 32.160041
## iter 60 value 28.179612
## iter 70 value 23.099466
## iter 80 value 20.666996
## iter 90 value 19.433885
## iter 100 value 18.812613
## final value 18.812613
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1480.873616
## iter 10 value 114.299332
## iter 20 value 61.501928
## iter 30 value 40.166622
## iter 40 value 23.155212
## iter 50 value 14.040416
## iter 60 value 10.274067
## iter 70 value 9.087053
## iter 80 value 8.416832
## iter 90 value 8.068070
## iter 100 value 7.791280
## final value 7.791280
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1337.056573
## iter 10 value 130.236983
## iter 20 value 77.089278
## iter 30 value 59.251346
## iter 40 value 41.761126
## iter 50 value 35.053963
## iter 60 value 32.163994
## iter 70 value 29.663763
## iter 80 value 28.100699
## iter 90 value 27.275945
## iter 100 value 26.375020
## final value 26.375020
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 731.867930
## iter 10 value 162.364573
## iter 20 value 95.716250
## iter 30 value 76.365062
## iter 40 value 70.082902
## iter 50 value 63.259417
## iter 60 value 57.683818
## iter 70 value 55.042665
## iter 80 value 52.581054
## iter 90 value 49.335049
## iter 100 value 43.920239
## final value 43.920239
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 720.464240
## iter 10 value 132.377738
## iter 20 value 77.475558
## iter 30 value 52.295722
## iter 40 value 32.233245
## iter 50 value 24.511211
## iter 60 value 22.130829
## iter 70 value 20.647207
## iter 80 value 19.625959
## iter 90 value 19.067073
## iter 100 value 18.455727
## final value 18.455727
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 994.942367
## iter 10 value 139.489939
## iter 20 value 82.726199
## iter 30 value 59.693095
## iter 40 value 53.251064
## iter 50 value 45.624392
## iter 60 value 42.372765
## iter 70 value 41.009867
## iter 80 value 36.092783
## iter 90 value 35.083461
## iter 100 value 34.651834
## final value 34.651834
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 718.044060
## iter 10 value 119.763882
## iter 20 value 75.606396
## iter 30 value 47.776392
## iter 40 value 32.611102
## iter 50 value 29.714777
## iter 60 value 24.801014
## iter 70 value 18.822698
## iter 80 value 16.879721
## iter 90 value 15.083869
## iter 100 value 13.614535
## final value 13.614535
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 969.545312
## iter 10 value 107.532859
## iter 20 value 68.292615
## iter 30 value 44.374401
## iter 40 value 30.050482
## iter 50 value 21.744737
## iter 60 value 19.347070
## iter 70 value 18.111552
## iter 80 value 17.709022
## iter 90 value 17.384284
## iter 100 value 17.149905
## final value 17.149905
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1274.148852
## iter 10 value 135.390362
## iter 20 value 84.345057
## iter 30 value 65.756191
## iter 40 value 52.829937
## iter 50 value 47.888379
## iter 60 value 46.182276
## iter 70 value 44.567339
## iter 80 value 41.479851
## iter 90 value 39.677854
## iter 100 value 39.011025
## final value 39.011025
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 883.503467
## iter 10 value 146.950569
## iter 20 value 99.800522
## iter 30 value 77.467913
## iter 40 value 63.686706
## iter 50 value 52.865081
## iter 60 value 44.112188
## iter 70 value 39.921784
## iter 80 value 38.444470
## iter 90 value 37.161190
## iter 100 value 35.454180
## final value 35.454180
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 736.413398
## iter 10 value 107.871913
## iter 20 value 60.341331
## iter 30 value 40.715343
## iter 40 value 30.688490
## iter 50 value 27.652550
## iter 60 value 24.209136
## iter 70 value 22.870356
## iter 80 value 21.623875
## iter 90 value 20.761122
## iter 100 value 20.214561
## final value 20.214561
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1115.101698
## iter 10 value 136.330693
## iter 20 value 66.339643
## iter 30 value 47.578957
## iter 40 value 35.231869
## iter 50 value 30.361403
## iter 60 value 28.322622
## iter 70 value 27.070896
## iter 80 value 25.676508
## iter 90 value 24.556233
## iter 100 value 24.224607
## final value 24.224607
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1132.296477
## iter 10 value 154.640044
## iter 20 value 111.436935
## iter 30 value 92.427753
## iter 40 value 82.368305
## iter 50 value 73.655168
## iter 60 value 68.595466
## iter 70 value 63.794059
## iter 80 value 58.899072
## iter 90 value 53.863184
## iter 100 value 51.444422
## final value 51.444422
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 780.222311
## iter 10 value 112.491795
## iter 20 value 69.607871
## iter 30 value 54.388149
## iter 40 value 44.480979
## iter 50 value 41.929979
## iter 60 value 39.745808
## iter 70 value 38.590747
## iter 80 value 35.217335
## iter 90 value 32.435333
## iter 100 value 30.653423
## final value 30.653423
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 844.315464
## iter 10 value 172.553177
## iter 20 value 118.216354
## iter 30 value 95.377488
## iter 40 value 80.766662
## iter 50 value 70.983436
## iter 60 value 65.080465
## iter 70 value 62.876391
## iter 80 value 59.982753
## iter 90 value 58.157213
## iter 100 value 55.853056
## final value 55.853056
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 813.820556
## iter 10 value 176.735531
## iter 20 value 94.199602
## iter 30 value 69.440571
## iter 40 value 47.249372
## iter 50 value 37.743345
## iter 60 value 33.703775
## iter 70 value 32.433814
## iter 80 value 30.604413
## iter 90 value 29.107477
## iter 100 value 27.689619
## final value 27.689619
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1042.900626
## iter 10 value 129.161115
## iter 20 value 83.981221
## iter 30 value 68.294333
## iter 40 value 60.041013
## iter 50 value 57.489980
## iter 60 value 56.473795
## iter 70 value 55.692239
## iter 80 value 55.172824
## iter 90 value 54.367818
## iter 100 value 53.966716
## final value 53.966716
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 717.991380
## iter 10 value 144.345574
## iter 20 value 72.514760
## iter 30 value 54.801269
## iter 40 value 36.916113
## iter 50 value 28.797515
## iter 60 value 27.355999
## iter 70 value 25.577596
## iter 80 value 22.358884
## iter 90 value 21.126822
## iter 100 value 20.440586
## final value 20.440586
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1349.921403
## iter 10 value 126.955048
## iter 20 value 83.703109
## iter 30 value 60.681808
## iter 40 value 41.861081
## iter 50 value 36.700924
## iter 60 value 33.942338
## iter 70 value 32.356780
## iter 80 value 31.072565
## iter 90 value 29.933116
## iter 100 value 29.027916
## final value 29.027916
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 876.870335
## iter 10 value 183.524266
## iter 20 value 92.973209
## iter 30 value 74.550930
## iter 40 value 49.847185
## iter 50 value 43.712979
## iter 60 value 41.022586
## iter 70 value 39.341253
## iter 80 value 37.962772
## iter 90 value 36.497213
## iter 100 value 33.009051
## final value 33.009051
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 953.233513
## iter 10 value 154.134730
## iter 20 value 110.545643
## iter 30 value 81.098197
## iter 40 value 69.256730
## iter 50 value 64.619137
## iter 60 value 62.122129
## iter 70 value 61.127790
## iter 80 value 60.658605
## iter 90 value 58.392526
## iter 100 value 56.852522
## final value 56.852522
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1084.691054
## iter 10 value 115.975012
## iter 20 value 70.020712
## iter 30 value 41.538210
## iter 40 value 34.011998
## iter 50 value 31.648410
## iter 60 value 30.150214
## iter 70 value 24.369037
## iter 80 value 20.893370
## iter 90 value 20.017412
## iter 100 value 19.318918
## final value 19.318918
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1480.422654
## iter 10 value 137.899044
## iter 20 value 86.987150
## iter 30 value 76.701365
## iter 40 value 69.950726
## iter 50 value 65.648188
## iter 60 value 64.202483
## iter 70 value 63.357573
## iter 80 value 62.570908
## iter 90 value 62.036155
## iter 100 value 61.622821
## final value 61.622821
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 915.371359
## iter 10 value 133.169843
## iter 20 value 91.644861
## iter 30 value 68.741231
## iter 40 value 50.511783
## iter 50 value 43.767245
## iter 60 value 39.279501
## iter 70 value 38.046900
## iter 80 value 34.691754
## iter 90 value 33.889729
## iter 100 value 32.925761
## final value 32.925761
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 612.432276
## iter 10 value 105.528719
## iter 20 value 69.748043
## iter 30 value 55.584598
## iter 40 value 50.840162
## iter 50 value 45.780670
## iter 60 value 43.038888
## iter 70 value 42.005894
## iter 80 value 41.038370
## iter 90 value 40.647454
## iter 100 value 39.820358
## final value 39.820358
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 791.611289
## iter 10 value 141.394431
## iter 20 value 91.315969
## iter 30 value 70.809137
## iter 40 value 58.569042
## iter 50 value 55.064937
## iter 60 value 53.330859
## iter 70 value 51.882163
## iter 80 value 50.825177
## iter 90 value 49.903481
## iter 100 value 49.514865
## final value 49.514865
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 899.500261
## iter 10 value 182.976118
## iter 20 value 115.182544
## iter 30 value 91.082302
## iter 40 value 77.788567
## iter 50 value 72.308987
## iter 60 value 68.466232
## iter 70 value 66.933042
## iter 80 value 65.355358
## iter 90 value 64.378141
## iter 100 value 63.912840
## final value 63.912840
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 843.390142
## iter 10 value 172.325322
## iter 20 value 91.032274
## iter 30 value 48.712986
## iter 40 value 32.612734
## iter 50 value 27.908284
## iter 60 value 25.412008
## iter 70 value 24.404378
## iter 80 value 23.122799
## iter 90 value 22.142991
## iter 100 value 20.705598
## final value 20.705598
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 685.986206
## iter 10 value 115.079235
## iter 20 value 71.307342
## iter 30 value 43.902246
## iter 40 value 26.022705
## iter 50 value 21.838969
## iter 60 value 21.111463
## iter 70 value 19.592066
## iter 80 value 18.145029
## iter 90 value 17.356991
## iter 100 value 16.395408
## final value 16.395408
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1715.869016
## iter 10 value 151.134019
## iter 20 value 67.149832
## iter 30 value 52.140637
## iter 40 value 36.552656
## iter 50 value 29.218936
## iter 60 value 26.755272
## iter 70 value 25.738501
## iter 80 value 23.842617
## iter 90 value 22.873563
## iter 100 value 21.011575
## final value 21.011575
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 973.834124
## iter 10 value 119.876193
## iter 20 value 57.229980
## iter 30 value 38.162901
## iter 40 value 26.032128
## iter 50 value 19.340770
## iter 60 value 15.087417
## iter 70 value 13.864697
## iter 80 value 13.248791
## iter 90 value 12.242137
## iter 100 value 11.357029
## final value 11.357029
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 768.203382
## iter 10 value 165.114855
## iter 20 value 114.814811
## iter 30 value 83.588648
## iter 40 value 62.654095
## iter 50 value 49.274570
## iter 60 value 43.799232
## iter 70 value 39.870257
## iter 80 value 38.364800
## iter 90 value 35.906100
## iter 100 value 33.104181
## final value 33.104181
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1132.683659
## iter 10 value 132.725889
## iter 20 value 83.648096
## iter 30 value 56.998306
## iter 40 value 42.095904
## iter 50 value 29.026993
## iter 60 value 21.883631
## iter 70 value 20.329321
## iter 80 value 19.657336
## iter 90 value 19.187257
## iter 100 value 18.503839
## final value 18.503839
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1318.061451
## iter 10 value 93.652280
## iter 20 value 48.605503
## iter 30 value 38.871825
## iter 40 value 32.330234
## iter 50 value 27.949793
## iter 60 value 26.285587
## iter 70 value 24.400598
## iter 80 value 23.174784
## iter 90 value 22.120761
## iter 100 value 21.413943
## final value 21.413943
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 749.434344
## iter 10 value 82.915603
## iter 20 value 44.335424
## iter 30 value 26.138166
## iter 40 value 22.557844
## iter 50 value 19.249625
## iter 60 value 18.685815
## iter 70 value 18.205269
## iter 80 value 17.835183
## iter 90 value 17.308113
## iter 100 value 17.077824
## final value 17.077824
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 750.771420
## iter 10 value 85.539636
## iter 20 value 50.774751
## iter 30 value 33.785440
## iter 40 value 25.276068
## iter 50 value 20.832315
## iter 60 value 19.575874
## iter 70 value 19.110008
## iter 80 value 18.746773
## iter 90 value 18.099872
## iter 100 value 17.485702
## final value 17.485702
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 837.935724
## iter 10 value 104.678860
## iter 20 value 70.331314
## iter 30 value 43.112192
## iter 40 value 36.010358
## iter 50 value 31.949608
## iter 60 value 28.512236
## iter 70 value 24.885878
## iter 80 value 21.490996
## iter 90 value 18.940057
## iter 100 value 16.899228
## final value 16.899228
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 733.061612
## iter 10 value 85.861931
## iter 20 value 40.896150
## iter 30 value 19.628036
## iter 40 value 8.301615
## iter 50 value 6.864015
## iter 60 value 5.777813
## iter 70 value 4.825645
## iter 80 value 4.044676
## iter 90 value 3.673086
## iter 100 value 3.329471
## final value 3.329471
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 804.656004
## iter 10 value 106.704614
## iter 20 value 78.745174
## iter 30 value 56.301049
## iter 40 value 50.987652
## iter 50 value 46.458547
## iter 60 value 37.736336
## iter 70 value 29.969998
## iter 80 value 23.620041
## iter 90 value 21.987130
## iter 100 value 20.360652
## final value 20.360652
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1371.529806
## iter 10 value 116.987375
## iter 20 value 69.404122
## iter 30 value 40.371780
## iter 40 value 30.982811
## iter 50 value 28.329761
## iter 60 value 25.356313
## iter 70 value 23.622488
## iter 80 value 21.953669
## iter 90 value 20.440078
## iter 100 value 17.596631
## final value 17.596631
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1359.123893
## iter 10 value 124.268957
## iter 20 value 79.845780
## iter 30 value 49.128022
## iter 40 value 32.450400
## iter 50 value 24.498899
## iter 60 value 22.178071
## iter 70 value 21.295359
## iter 80 value 19.491065
## iter 90 value 17.951807
## iter 100 value 16.619092
## final value 16.619092
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 794.291917
## iter 10 value 120.249749
## iter 20 value 65.003131
## iter 30 value 46.584542
## iter 40 value 26.183625
## iter 50 value 20.398880
## iter 60 value 17.536538
## iter 70 value 16.941450
## iter 80 value 16.230882
## iter 90 value 15.479427
## iter 100 value 14.761603
## final value 14.761603
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1058.257891
## iter 10 value 127.617488
## iter 20 value 61.804877
## iter 30 value 41.585867
## iter 40 value 26.872200
## iter 50 value 16.885778
## iter 60 value 11.471700
## iter 70 value 10.731752
## iter 80 value 10.250125
## iter 90 value 9.933431
## iter 100 value 9.684371
## final value 9.684371
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1139.876386
## iter 10 value 122.927424
## iter 20 value 66.026908
## iter 30 value 50.690813
## iter 40 value 36.567292
## iter 50 value 27.503259
## iter 60 value 20.296898
## iter 70 value 16.553138
## iter 80 value 15.287854
## iter 90 value 14.317042
## iter 100 value 13.958394
## final value 13.958394
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1455.578618
## iter 10 value 121.285943
## iter 20 value 84.571101
## iter 30 value 57.515920
## iter 40 value 36.479862
## iter 50 value 25.587390
## iter 60 value 22.521045
## iter 70 value 19.956009
## iter 80 value 17.860525
## iter 90 value 16.030331
## iter 100 value 14.512462
## final value 14.512462
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1720.210392
## iter 10 value 112.036497
## iter 20 value 62.702133
## iter 30 value 36.019148
## iter 40 value 20.402283
## iter 50 value 16.949159
## iter 60 value 16.005131
## iter 70 value 15.371601
## iter 80 value 14.986186
## iter 90 value 14.514148
## iter 100 value 14.146713
## final value 14.146713
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 616.488965
## iter 10 value 110.475508
## iter 20 value 83.164876
## iter 30 value 64.794793
## iter 40 value 53.181846
## iter 50 value 44.267023
## iter 60 value 36.318955
## iter 70 value 32.053900
## iter 80 value 28.964126
## iter 90 value 26.265002
## iter 100 value 22.889698
## final value 22.889698
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 980.636493
## iter 10 value 108.396637
## iter 20 value 63.636263
## iter 30 value 53.703427
## iter 40 value 48.536347
## iter 50 value 44.453316
## iter 60 value 41.075111
## iter 70 value 39.287900
## iter 80 value 37.943893
## iter 90 value 37.151393
## iter 100 value 36.148549
## final value 36.148549
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1107.660839
## iter 10 value 148.891918
## iter 20 value 68.998608
## iter 30 value 50.986302
## iter 40 value 41.609705
## iter 50 value 39.854296
## iter 60 value 34.440546
## iter 70 value 32.853409
## iter 80 value 31.583099
## iter 90 value 30.496940
## iter 100 value 29.647112
## final value 29.647112
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1070.092134
## iter 10 value 122.826802
## iter 20 value 88.389571
## iter 30 value 63.082926
## iter 40 value 49.943082
## iter 50 value 40.930214
## iter 60 value 37.794466
## iter 70 value 31.953884
## iter 80 value 28.089302
## iter 90 value 26.283530
## iter 100 value 25.836893
## final value 25.836893
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1010.336075
## iter 10 value 169.700812
## iter 20 value 114.672911
## iter 30 value 82.039452
## iter 40 value 70.709010
## iter 50 value 63.020304
## iter 60 value 59.701078
## iter 70 value 58.483494
## iter 80 value 58.013394
## iter 90 value 56.186726
## iter 100 value 55.622668
## final value 55.622668
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 832.670736
## iter 10 value 121.469375
## iter 20 value 79.659380
## iter 30 value 60.952257
## iter 40 value 52.372410
## iter 50 value 46.472346
## iter 60 value 42.957023
## iter 70 value 41.551713
## iter 80 value 40.928143
## iter 90 value 40.747873
## iter 100 value 40.532045
## final value 40.532045
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 653.838913
## iter 10 value 99.921634
## iter 20 value 65.453164
## iter 30 value 43.204345
## iter 40 value 32.100364
## iter 50 value 29.531816
## iter 60 value 28.204926
## iter 70 value 27.318692
## iter 80 value 23.756592
## iter 90 value 20.363891
## iter 100 value 16.683636
## final value 16.683636
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 650.126645
## iter 10 value 131.988070
## iter 20 value 72.301641
## iter 30 value 46.841182
## iter 40 value 37.543340
## iter 50 value 34.897372
## iter 60 value 31.752015
## iter 70 value 30.190234
## iter 80 value 28.964445
## iter 90 value 27.958008
## iter 100 value 26.923890
## final value 26.923890
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 681.574119
## iter 10 value 112.713191
## iter 20 value 67.027324
## iter 30 value 42.213726
## iter 40 value 30.351256
## iter 50 value 24.123072
## iter 60 value 22.041279
## iter 70 value 20.374832
## iter 80 value 19.686630
## iter 90 value 19.414022
## iter 100 value 19.067261
## final value 19.067261
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 696.236415
## iter 10 value 114.963697
## iter 20 value 73.508599
## iter 30 value 45.439138
## iter 40 value 36.813298
## iter 50 value 33.130904
## iter 60 value 31.226751
## iter 70 value 30.068554
## iter 80 value 29.082316
## iter 90 value 28.221512
## iter 100 value 27.372681
## final value 27.372681
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1087.599283
## iter 10 value 177.897319
## iter 20 value 101.864014
## iter 30 value 73.555360
## iter 40 value 64.557180
## iter 50 value 58.195372
## iter 60 value 54.470049
## iter 70 value 52.842115
## iter 80 value 52.053511
## iter 90 value 49.764329
## iter 100 value 49.141609
## final value 49.141609
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1525.142569
## iter 10 value 92.107558
## iter 20 value 64.730348
## iter 30 value 49.067027
## iter 40 value 32.857310
## iter 50 value 17.695426
## iter 60 value 14.387571
## iter 70 value 13.047550
## iter 80 value 12.209245
## iter 90 value 11.603386
## iter 100 value 11.246300
## final value 11.246300
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 884.811872
## iter 10 value 124.608178
## iter 20 value 80.172122
## iter 30 value 60.333323
## iter 40 value 43.215675
## iter 50 value 26.964605
## iter 60 value 22.165269
## iter 70 value 16.734103
## iter 80 value 14.657109
## iter 90 value 13.661108
## iter 100 value 12.943769
## final value 12.943769
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1432.413150
## iter 10 value 115.999090
## iter 20 value 64.448248
## iter 30 value 35.265756
## iter 40 value 26.566883
## iter 50 value 22.087279
## iter 60 value 20.390192
## iter 70 value 19.898979
## iter 80 value 19.641180
## iter 90 value 15.895854
## iter 100 value 14.862449
## final value 14.862449
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1273.115969
## iter 10 value 110.660713
## iter 20 value 70.557341
## iter 30 value 45.911926
## iter 40 value 35.307489
## iter 50 value 32.225676
## iter 60 value 30.903968
## iter 70 value 29.895179
## iter 80 value 28.976739
## iter 90 value 28.417181
## iter 100 value 27.773702
## final value 27.773702
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1569.206773
## iter 10 value 101.990725
## iter 20 value 61.625195
## iter 30 value 37.218704
## iter 40 value 30.166674
## iter 50 value 28.375105
## iter 60 value 26.581261
## iter 70 value 25.021100
## iter 80 value 22.855166
## iter 90 value 21.886564
## iter 100 value 21.105587
## final value 21.105587
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 749.045361
## iter 10 value 124.840275
## iter 20 value 71.304909
## iter 30 value 55.231656
## iter 40 value 40.310612
## iter 50 value 34.155307
## iter 60 value 32.616759
## iter 70 value 30.062087
## iter 80 value 29.495304
## iter 90 value 29.100205
## iter 100 value 28.935975
## final value 28.935975
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1074.144073
## iter 10 value 122.602564
## iter 20 value 79.023683
## iter 30 value 51.396323
## iter 40 value 38.737339
## iter 50 value 34.082656
## iter 60 value 29.541621
## iter 70 value 25.270752
## iter 80 value 23.957586
## iter 90 value 23.086291
## iter 100 value 22.625450
## final value 22.625450
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 729.110515
## iter 10 value 120.461690
## iter 20 value 68.128918
## iter 30 value 48.519165
## iter 40 value 40.737358
## iter 50 value 38.880868
## iter 60 value 36.491762
## iter 70 value 35.447867
## iter 80 value 34.561267
## iter 90 value 33.606879
## iter 100 value 30.233050
## final value 30.233050
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1038.511571
## iter 10 value 136.713879
## iter 20 value 78.740257
## iter 30 value 58.141732
## iter 40 value 46.261575
## iter 50 value 40.781748
## iter 60 value 36.520676
## iter 70 value 34.852384
## iter 80 value 33.759070
## iter 90 value 32.488329
## iter 100 value 28.999347
## final value 28.999347
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 898.125716
## iter 10 value 103.055729
## iter 20 value 62.446427
## iter 30 value 47.548532
## iter 40 value 41.102597
## iter 50 value 36.538746
## iter 60 value 33.654752
## iter 70 value 32.000368
## iter 80 value 31.410308
## iter 90 value 29.854406
## iter 100 value 26.499913
## final value 26.499913
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 696.997977
## iter 10 value 204.845486
## iter 20 value 84.713490
## iter 30 value 57.521070
## iter 40 value 46.184901
## iter 50 value 41.956975
## iter 60 value 39.634539
## iter 70 value 37.654539
## iter 80 value 34.544526
## iter 90 value 33.326978
## iter 100 value 31.376764
## final value 31.376764
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 853.786188
## iter 10 value 136.069554
## iter 20 value 80.937084
## iter 30 value 59.088447
## iter 40 value 45.390834
## iter 50 value 37.643482
## iter 60 value 34.271448
## iter 70 value 32.083538
## iter 80 value 30.121449
## iter 90 value 29.004795
## iter 100 value 28.528909
## final value 28.528909
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 724.556141
## iter 10 value 97.220745
## iter 20 value 66.112889
## iter 30 value 44.708383
## iter 40 value 30.161878
## iter 50 value 24.709908
## iter 60 value 21.915945
## iter 70 value 20.446111
## iter 80 value 19.999900
## iter 90 value 19.688376
## iter 100 value 19.235186
## final value 19.235186
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 722.650904
## iter 10 value 131.248207
## iter 20 value 72.174544
## iter 30 value 33.414209
## iter 40 value 15.338560
## iter 50 value 11.434316
## iter 60 value 10.739429
## iter 70 value 10.199042
## iter 80 value 8.985894
## iter 90 value 8.742954
## iter 100 value 8.600469
## final value 8.600469
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 929.604183
## iter 10 value 132.715706
## iter 20 value 83.238226
## iter 30 value 37.807988
## iter 40 value 22.889731
## iter 50 value 19.862385
## iter 60 value 17.115762
## iter 70 value 15.848101
## iter 80 value 14.849491
## iter 90 value 13.886712
## iter 100 value 12.875573
## final value 12.875573
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1156.311483
## iter 10 value 110.665917
## iter 20 value 70.741795
## iter 30 value 49.154899
## iter 40 value 31.097041
## iter 50 value 24.443584
## iter 60 value 23.175117
## iter 70 value 22.696506
## iter 80 value 19.327775
## iter 90 value 17.517186
## iter 100 value 15.730071
## final value 15.730071
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 688.586228
## iter 10 value 113.522736
## iter 20 value 66.955265
## iter 30 value 35.537672
## iter 40 value 26.859962
## iter 50 value 24.931386
## iter 60 value 22.850959
## iter 70 value 20.674771
## iter 80 value 16.959991
## iter 90 value 14.035427
## iter 100 value 10.824480
## final value 10.824480
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1287.437465
## iter 10 value 152.398223
## iter 20 value 81.182216
## iter 30 value 52.337860
## iter 40 value 33.207766
## iter 50 value 22.078765
## iter 60 value 18.589640
## iter 70 value 16.993234
## iter 80 value 15.596991
## iter 90 value 13.967097
## iter 100 value 13.061958
## final value 13.061958
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 727.766343
## iter 10 value 81.888791
## iter 20 value 45.675003
## iter 30 value 28.756903
## iter 40 value 21.464436
## iter 50 value 18.713100
## iter 60 value 17.804659
## iter 70 value 16.428548
## iter 80 value 15.269225
## iter 90 value 14.483359
## iter 100 value 13.068988
## final value 13.068988
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1034.406938
## iter 10 value 111.843864
## iter 20 value 64.512884
## iter 30 value 48.180492
## iter 40 value 37.578619
## iter 50 value 32.076349
## iter 60 value 29.571327
## iter 70 value 28.562348
## iter 80 value 28.105055
## iter 90 value 27.762828
## iter 100 value 27.246229
## final value 27.246229
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1239.788513
## iter 10 value 101.131070
## iter 20 value 46.288456
## iter 30 value 28.650358
## iter 40 value 23.407402
## iter 50 value 19.877366
## iter 60 value 17.972947
## iter 70 value 15.854730
## iter 80 value 14.736527
## iter 90 value 13.980372
## iter 100 value 13.582358
## final value 13.582358
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 948.519969
## iter 10 value 89.414179
## iter 20 value 50.407962
## iter 30 value 27.445512
## iter 40 value 17.340545
## iter 50 value 13.630646
## iter 60 value 12.967318
## iter 70 value 12.466504
## iter 80 value 12.181103
## iter 90 value 11.710975
## iter 100 value 11.422071
## final value 11.422071
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 997.390482
## iter 10 value 107.803848
## iter 20 value 61.225402
## iter 30 value 35.994448
## iter 40 value 15.930021
## iter 50 value 7.796505
## iter 60 value 4.917565
## iter 70 value 4.009718
## iter 80 value 3.422907
## iter 90 value 3.078035
## iter 100 value 2.909469
## final value 2.909469
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 892.576310
## iter 10 value 105.531565
## iter 20 value 71.842812
## iter 30 value 49.634674
## iter 40 value 34.557683
## iter 50 value 23.024306
## iter 60 value 18.712422
## iter 70 value 17.600765
## iter 80 value 16.783457
## iter 90 value 16.280489
## iter 100 value 15.916382
## final value 15.916382
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 963.716384
## iter 10 value 98.889697
## iter 20 value 59.897267
## iter 30 value 47.360335
## iter 40 value 34.322169
## iter 50 value 22.352148
## iter 60 value 19.901126
## iter 70 value 18.412202
## iter 80 value 17.862066
## iter 90 value 16.154628
## iter 100 value 14.982026
## final value 14.982026
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 668.164548
## iter 10 value 106.694854
## iter 20 value 68.686723
## iter 30 value 37.599516
## iter 40 value 27.389322
## iter 50 value 23.386028
## iter 60 value 21.976666
## iter 70 value 19.318741
## iter 80 value 17.563898
## iter 90 value 16.844834
## iter 100 value 16.417868
## final value 16.417868
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 610.588377
## iter 10 value 103.066189
## iter 20 value 54.860827
## iter 30 value 33.723727
## iter 40 value 23.665686
## iter 50 value 19.443873
## iter 60 value 16.503197
## iter 70 value 13.619768
## iter 80 value 12.608799
## iter 90 value 11.960736
## iter 100 value 10.123797
## final value 10.123797
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 797.042201
## iter 10 value 120.956297
## iter 20 value 81.773114
## iter 30 value 50.995370
## iter 40 value 35.336489
## iter 50 value 30.276480
## iter 60 value 27.873624
## iter 70 value 26.235917
## iter 80 value 25.199752
## iter 90 value 20.967634
## iter 100 value 19.493573
## final value 19.493573
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 912.850286
## iter 10 value 120.340255
## iter 20 value 72.183522
## iter 30 value 39.262645
## iter 40 value 33.010396
## iter 50 value 30.084487
## iter 60 value 28.878122
## iter 70 value 28.666724
## iter 80 value 28.438601
## iter 90 value 28.302606
## iter 100 value 28.238110
## final value 28.238110
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1510.936265
## iter 10 value 121.093622
## iter 20 value 86.204212
## iter 30 value 65.299265
## iter 40 value 53.869891
## iter 50 value 42.818379
## iter 60 value 36.356852
## iter 70 value 31.383731
## iter 80 value 28.083352
## iter 90 value 26.787592
## iter 100 value 24.770384
## final value 24.770384
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 855.364026
## iter 10 value 150.790808
## iter 20 value 89.946132
## iter 30 value 68.797589
## iter 40 value 53.096679
## iter 50 value 46.800157
## iter 60 value 43.753812
## iter 70 value 41.133787
## iter 80 value 39.503124
## iter 90 value 37.637370
## iter 100 value 36.755099
## final value 36.755099
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 750.728629
## iter 10 value 151.310473
## iter 20 value 105.000577
## iter 30 value 88.771554
## iter 40 value 74.780331
## iter 50 value 66.098472
## iter 60 value 60.735240
## iter 70 value 56.058464
## iter 80 value 51.632791
## iter 90 value 47.940664
## iter 100 value 46.060296
## final value 46.060296
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 936.917719
## iter 10 value 113.968599
## iter 20 value 77.768396
## iter 30 value 53.772356
## iter 40 value 39.384883
## iter 50 value 29.393870
## iter 60 value 22.593457
## iter 70 value 19.893682
## iter 80 value 18.949812
## iter 90 value 18.365404
## iter 100 value 17.874060
## final value 17.874060
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1114.591910
## iter 10 value 152.868193
## iter 20 value 67.299463
## iter 30 value 44.483335
## iter 40 value 29.504361
## iter 50 value 24.055573
## iter 60 value 22.024497
## iter 70 value 21.440002
## iter 80 value 20.757315
## iter 90 value 18.939161
## iter 100 value 18.485217
## final value 18.485217
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 780.230103
## iter 10 value 127.682431
## iter 20 value 71.272620
## iter 30 value 48.893409
## iter 40 value 37.393835
## iter 50 value 33.601114
## iter 60 value 31.126520
## iter 70 value 29.559986
## iter 80 value 24.421898
## iter 90 value 21.743054
## iter 100 value 20.754517
## final value 20.754517
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1515.431397
## iter 10 value 130.884565
## iter 20 value 91.302021
## iter 30 value 75.887223
## iter 40 value 62.412428
## iter 50 value 54.511673
## iter 60 value 50.695558
## iter 70 value 48.857288
## iter 80 value 47.419206
## iter 90 value 46.848443
## iter 100 value 46.260494
## final value 46.260494
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1008.111161
## iter 10 value 96.152441
## iter 20 value 40.661552
## iter 30 value 20.901792
## iter 40 value 14.622708
## iter 50 value 12.686072
## iter 60 value 11.993068
## iter 70 value 11.528511
## iter 80 value 11.221383
## iter 90 value 10.870953
## iter 100 value 10.403879
## final value 10.403879
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1108.390293
## iter 10 value 136.791550
## iter 20 value 67.226756
## iter 30 value 58.526213
## iter 40 value 50.366540
## iter 50 value 46.516933
## iter 60 value 43.343318
## iter 70 value 40.051354
## iter 80 value 35.603422
## iter 90 value 32.038444
## iter 100 value 30.213831
## final value 30.213831
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 939.128006
## iter 10 value 93.527310
## iter 20 value 51.081082
## iter 30 value 44.601290
## iter 40 value 40.155787
## iter 50 value 35.977829
## iter 60 value 31.471635
## iter 70 value 30.234153
## iter 80 value 29.223352
## iter 90 value 27.323845
## iter 100 value 25.729100
## final value 25.729100
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1078.052192
## iter 10 value 106.541726
## iter 20 value 74.800148
## iter 30 value 61.206327
## iter 40 value 52.359180
## iter 50 value 47.897122
## iter 60 value 46.598965
## iter 70 value 44.842714
## iter 80 value 43.310605
## iter 90 value 43.096372
## iter 100 value 42.243832
## final value 42.243832
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1135.595143
## iter 10 value 97.835319
## iter 20 value 57.381881
## iter 30 value 37.267519
## iter 40 value 31.573768
## iter 50 value 27.649919
## iter 60 value 26.807899
## iter 70 value 25.953230
## iter 80 value 25.282367
## iter 90 value 24.264768
## iter 100 value 23.736681
## final value 23.736681
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 923.130964
## iter 10 value 111.012916
## iter 20 value 75.848854
## iter 30 value 54.567402
## iter 40 value 44.212329
## iter 50 value 40.690814
## iter 60 value 37.439021
## iter 70 value 35.648045
## iter 80 value 34.268674
## iter 90 value 32.301537
## iter 100 value 31.280747
## final value 31.280747
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 667.413986
## iter 10 value 78.548647
## iter 20 value 41.075636
## iter 30 value 23.502743
## iter 40 value 13.822720
## iter 50 value 9.484292
## iter 60 value 8.302038
## iter 70 value 7.807672
## iter 80 value 7.363621
## iter 90 value 7.199705
## iter 100 value 7.086166
## final value 7.086166
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1204.935713
## iter 10 value 96.001703
## iter 20 value 58.082517
## iter 30 value 36.127736
## iter 40 value 21.724880
## iter 50 value 18.098742
## iter 60 value 16.334650
## iter 70 value 15.092416
## iter 80 value 14.372863
## iter 90 value 13.895955
## iter 100 value 13.512741
## final value 13.512741
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 862.490912
## iter 10 value 81.052163
## iter 20 value 62.670007
## iter 30 value 50.845399
## iter 40 value 40.558804
## iter 50 value 32.919504
## iter 60 value 26.265535
## iter 70 value 24.109629
## iter 80 value 22.735471
## iter 90 value 21.736606
## iter 100 value 20.116441
## final value 20.116441
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 643.919011
## iter 10 value 122.155329
## iter 20 value 62.678522
## iter 30 value 33.859162
## iter 40 value 21.230155
## iter 50 value 17.471724
## iter 60 value 14.874140
## iter 70 value 12.241103
## iter 80 value 11.044352
## iter 90 value 10.272909
## iter 100 value 9.388053
## final value 9.388053
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 948.222265
## iter 10 value 111.493613
## iter 20 value 57.271408
## iter 30 value 40.915141
## iter 40 value 32.629565
## iter 50 value 24.378764
## iter 60 value 16.441118
## iter 70 value 13.134538
## iter 80 value 12.044945
## iter 90 value 11.130367
## iter 100 value 10.491701
## final value 10.491701
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 913.968713
## iter 10 value 142.426030
## iter 20 value 86.546005
## iter 30 value 49.509434
## iter 40 value 27.570277
## iter 50 value 20.834858
## iter 60 value 19.442306
## iter 70 value 18.324156
## iter 80 value 17.646262
## iter 90 value 17.105379
## iter 100 value 16.538233
## final value 16.538233
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 779.211598
## iter 10 value 88.103385
## iter 20 value 55.320229
## iter 30 value 38.846420
## iter 40 value 30.123373
## iter 50 value 22.086522
## iter 60 value 18.844106
## iter 70 value 17.048241
## iter 80 value 15.926693
## iter 90 value 15.317315
## iter 100 value 14.319306
## final value 14.319306
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 995.158472
## iter 10 value 89.270473
## iter 20 value 49.688178
## iter 30 value 30.938962
## iter 40 value 21.741238
## iter 50 value 17.003384
## iter 60 value 12.647884
## iter 70 value 10.859148
## iter 80 value 9.673285
## iter 90 value 9.076578
## iter 100 value 8.664871
## final value 8.664871
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 885.467299
## iter 10 value 103.017255
## iter 20 value 57.832779
## iter 30 value 33.891073
## iter 40 value 25.215178
## iter 50 value 22.947245
## iter 60 value 22.241070
## iter 70 value 21.306155
## iter 80 value 19.949347
## iter 90 value 19.194567
## iter 100 value 18.212934
## final value 18.212934
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 706.150677
## iter 10 value 87.695295
## iter 20 value 58.281906
## iter 30 value 31.151307
## iter 40 value 26.132744
## iter 50 value 21.969520
## iter 60 value 20.059689
## iter 70 value 19.254434
## iter 80 value 18.944450
## iter 90 value 18.808251
## iter 100 value 18.732225
## final value 18.732225
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 757.437885
## iter 10 value 113.395104
## iter 20 value 62.828076
## iter 30 value 37.341518
## iter 40 value 22.578811
## iter 50 value 14.984327
## iter 60 value 13.653442
## iter 70 value 11.871841
## iter 80 value 10.889926
## iter 90 value 10.135877
## iter 100 value 8.255826
## final value 8.255826
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1585.148710
## iter 10 value 127.616696
## iter 20 value 90.115222
## iter 30 value 75.996128
## iter 40 value 57.682928
## iter 50 value 32.675457
## iter 60 value 24.027085
## iter 70 value 20.018827
## iter 80 value 18.343391
## iter 90 value 17.533152
## iter 100 value 16.696660
## final value 16.696660
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1157.164870
## iter 10 value 110.573224
## iter 20 value 63.356354
## iter 30 value 38.343576
## iter 40 value 29.822137
## iter 50 value 26.535151
## iter 60 value 25.581674
## iter 70 value 24.580505
## iter 80 value 23.843987
## iter 90 value 23.241075
## iter 100 value 22.791695
## final value 22.791695
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1084.526944
## iter 10 value 127.214003
## iter 20 value 90.898793
## iter 30 value 67.703175
## iter 40 value 49.420160
## iter 50 value 39.770405
## iter 60 value 36.888431
## iter 70 value 34.580019
## iter 80 value 33.713377
## iter 90 value 33.111045
## iter 100 value 32.318723
## final value 32.318723
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 665.639222
## iter 10 value 134.456155
## iter 20 value 91.788850
## iter 30 value 75.098583
## iter 40 value 68.141223
## iter 50 value 57.486471
## iter 60 value 52.040760
## iter 70 value 50.690541
## iter 80 value 48.663508
## iter 90 value 45.922657
## iter 100 value 44.815995
## final value 44.815995
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 729.271194
## iter 10 value 131.337393
## iter 20 value 82.306425
## iter 30 value 47.683303
## iter 40 value 34.299458
## iter 50 value 32.521808
## iter 60 value 30.882733
## iter 70 value 26.898135
## iter 80 value 24.735155
## iter 90 value 23.951567
## iter 100 value 23.288320
## final value 23.288320
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 743.321953
## iter 10 value 156.866550
## iter 20 value 85.813922
## iter 30 value 52.634102
## iter 40 value 37.927402
## iter 50 value 26.141612
## iter 60 value 20.326364
## iter 70 value 18.321162
## iter 80 value 16.061087
## iter 90 value 14.385197
## iter 100 value 13.219578
## final value 13.219578
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1426.599463
## iter 10 value 175.870833
## iter 20 value 109.659830
## iter 30 value 81.095574
## iter 40 value 69.605113
## iter 50 value 64.774765
## iter 60 value 59.124263
## iter 70 value 51.516351
## iter 80 value 49.647411
## iter 90 value 48.776320
## iter 100 value 48.437156
## final value 48.437156
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1164.532556
## iter 10 value 101.497182
## iter 20 value 62.378526
## iter 30 value 37.135040
## iter 40 value 20.823781
## iter 50 value 16.001275
## iter 60 value 13.459576
## iter 70 value 11.647172
## iter 80 value 10.094377
## iter 90 value 9.094751
## iter 100 value 7.463370
## final value 7.463370
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 747.068277
## iter 10 value 122.038151
## iter 20 value 69.893956
## iter 30 value 42.198125
## iter 40 value 22.440635
## iter 50 value 15.532606
## iter 60 value 13.990352
## iter 70 value 13.602359
## iter 80 value 13.407468
## iter 90 value 13.243972
## iter 100 value 12.957137
## final value 12.957137
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1439.373488
## iter 10 value 120.869695
## iter 20 value 73.012444
## iter 30 value 48.867673
## iter 40 value 31.528316
## iter 50 value 20.449706
## iter 60 value 15.849922
## iter 70 value 13.712143
## iter 80 value 12.846147
## iter 90 value 12.307429
## iter 100 value 11.624237
## final value 11.624237
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 810.198878
## iter 10 value 111.614347
## iter 20 value 73.606754
## iter 30 value 42.616721
## iter 40 value 28.023017
## iter 50 value 23.645841
## iter 60 value 21.138176
## iter 70 value 18.561554
## iter 80 value 16.908797
## iter 90 value 12.043844
## iter 100 value 11.356238
## final value 11.356238
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 929.342127
## iter 10 value 231.628582
## iter 20 value 145.886230
## iter 30 value 103.311282
## iter 40 value 80.200437
## iter 50 value 67.287448
## iter 60 value 57.834562
## iter 70 value 51.291319
## iter 80 value 48.796893
## iter 90 value 46.301744
## iter 100 value 43.309415
## final value 43.309415
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 803.203668
## iter 10 value 140.468958
## iter 20 value 106.459005
## iter 30 value 80.953560
## iter 40 value 64.230508
## iter 50 value 56.087502
## iter 60 value 49.901381
## iter 70 value 42.490812
## iter 80 value 40.384516
## iter 90 value 39.195897
## iter 100 value 38.773849
## final value 38.773849
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 915.344937
## iter 10 value 140.760314
## iter 20 value 102.347888
## iter 30 value 67.224318
## iter 40 value 52.135375
## iter 50 value 34.908184
## iter 60 value 28.129830
## iter 70 value 26.433109
## iter 80 value 23.193932
## iter 90 value 22.261079
## iter 100 value 21.608107
## final value 21.608107
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 674.909587
## iter 10 value 151.118601
## iter 20 value 81.975875
## iter 30 value 51.807425
## iter 40 value 42.732641
## iter 50 value 36.725790
## iter 60 value 31.570441
## iter 70 value 27.959704
## iter 80 value 26.348078
## iter 90 value 25.415006
## iter 100 value 24.665755
## final value 24.665755
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1143.054866
## iter 10 value 154.988946
## iter 20 value 83.377842
## iter 30 value 57.643809
## iter 40 value 38.562837
## iter 50 value 34.141108
## iter 60 value 30.474764
## iter 70 value 28.498638
## iter 80 value 27.696195
## iter 90 value 27.133768
## iter 100 value 25.615499
## final value 25.615499
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1038.101159
## iter 10 value 157.830366
## iter 20 value 89.791628
## iter 30 value 74.148015
## iter 40 value 67.829965
## iter 50 value 58.118114
## iter 60 value 49.537422
## iter 70 value 36.373264
## iter 80 value 27.412029
## iter 90 value 24.421534
## iter 100 value 21.568804
## final value 21.568804
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1870.392804
## iter 10 value 111.742293
## iter 20 value 78.416774
## iter 30 value 37.413716
## iter 40 value 22.419051
## iter 50 value 18.530898
## iter 60 value 16.195441
## iter 70 value 14.857734
## iter 80 value 14.020504
## iter 90 value 13.276830
## iter 100 value 12.652003
## final value 12.652003
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1359.567476
## iter 10 value 184.724612
## iter 20 value 105.368775
## iter 30 value 75.651926
## iter 40 value 62.774582
## iter 50 value 54.409675
## iter 60 value 49.585730
## iter 70 value 47.305445
## iter 80 value 45.098368
## iter 90 value 43.412867
## iter 100 value 42.424167
## final value 42.424167
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 615.957742
## iter 10 value 123.537621
## iter 20 value 85.126385
## iter 30 value 65.341559
## iter 40 value 58.134595
## iter 50 value 53.035701
## iter 60 value 47.527380
## iter 70 value 44.241125
## iter 80 value 42.273946
## iter 90 value 39.356986
## iter 100 value 38.628085
## final value 38.628085
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1277.697201
## iter 10 value 116.587728
## iter 20 value 67.518117
## iter 30 value 45.466968
## iter 40 value 28.056694
## iter 50 value 20.773892
## iter 60 value 15.674598
## iter 70 value 14.318682
## iter 80 value 13.387719
## iter 90 value 12.738792
## iter 100 value 11.998247
## final value 11.998247
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1271.590418
## iter 10 value 157.555430
## iter 20 value 85.945015
## iter 30 value 68.739034
## iter 40 value 59.911231
## iter 50 value 57.654448
## iter 60 value 55.424772
## iter 70 value 53.490276
## iter 80 value 51.088083
## iter 90 value 46.502533
## iter 100 value 43.579574
## final value 43.579574
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 774.240900
## iter 10 value 107.269381
## iter 20 value 63.069436
## iter 30 value 37.196042
## iter 40 value 26.629742
## iter 50 value 21.490513
## iter 60 value 19.971261
## iter 70 value 18.310898
## iter 80 value 17.444658
## iter 90 value 15.884510
## iter 100 value 13.262066
## final value 13.262066
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 653.605989
## iter 10 value 90.714175
## iter 20 value 59.783598
## iter 30 value 32.952253
## iter 40 value 23.783633
## iter 50 value 22.034944
## iter 60 value 21.363951
## iter 70 value 20.921917
## iter 80 value 20.640851
## iter 90 value 20.505943
## iter 100 value 20.452005
## final value 20.452005
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1664.401219
## iter 10 value 229.251579
## iter 20 value 132.552491
## iter 30 value 85.149663
## iter 40 value 71.661893
## iter 50 value 61.482888
## iter 60 value 54.857272
## iter 70 value 50.697813
## iter 80 value 48.640810
## iter 90 value 47.051366
## iter 100 value 46.569148
## final value 46.569148
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 873.281962
## iter 10 value 127.996767
## iter 20 value 79.572446
## iter 30 value 58.201108
## iter 40 value 45.935450
## iter 50 value 41.766729
## iter 60 value 38.529226
## iter 70 value 36.154743
## iter 80 value 34.992412
## iter 90 value 34.163193
## iter 100 value 32.597669
## final value 32.597669
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 682.284859
## iter 10 value 122.048590
## iter 20 value 82.289697
## iter 30 value 60.483383
## iter 40 value 46.008179
## iter 50 value 38.958008
## iter 60 value 36.497061
## iter 70 value 34.488804
## iter 80 value 32.984849
## iter 90 value 31.854973
## iter 100 value 30.606523
## final value 30.606523
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1006.224055
## iter 10 value 149.227806
## iter 20 value 79.727257
## iter 30 value 63.847386
## iter 40 value 57.779368
## iter 50 value 54.845354
## iter 60 value 52.662973
## iter 70 value 50.913059
## iter 80 value 49.620423
## iter 90 value 47.543424
## iter 100 value 46.841143
## final value 46.841143
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 903.803449
## iter 10 value 129.652308
## iter 20 value 86.813968
## iter 30 value 72.907355
## iter 40 value 66.520217
## iter 50 value 62.480161
## iter 60 value 59.306847
## iter 70 value 56.884145
## iter 80 value 55.531989
## iter 90 value 54.226416
## iter 100 value 53.082079
## final value 53.082079
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 894.168680
## iter 10 value 112.414883
## iter 20 value 62.849938
## iter 30 value 32.082062
## iter 40 value 15.055072
## iter 50 value 10.282712
## iter 60 value 8.784533
## iter 70 value 8.156645
## iter 80 value 7.735823
## iter 90 value 7.056352
## iter 100 value 6.662838
## final value 6.662838
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1232.675698
## iter 10 value 146.780932
## iter 20 value 97.282190
## iter 30 value 71.442856
## iter 40 value 54.406879
## iter 50 value 45.052354
## iter 60 value 40.150246
## iter 70 value 38.086895
## iter 80 value 36.883451
## iter 90 value 35.344982
## iter 100 value 34.536042
## final value 34.536042
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 788.185431
## iter 10 value 92.202446
## iter 20 value 45.770256
## iter 30 value 22.055105
## iter 40 value 14.463348
## iter 50 value 12.264601
## iter 60 value 11.492775
## iter 70 value 10.360196
## iter 80 value 9.593864
## iter 90 value 8.501445
## iter 100 value 8.004739
## final value 8.004739
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 967.774420
## iter 10 value 154.154151
## iter 20 value 75.140227
## iter 30 value 47.632284
## iter 40 value 37.904909
## iter 50 value 34.243007
## iter 60 value 32.580468
## iter 70 value 31.142801
## iter 80 value 30.046356
## iter 90 value 29.096072
## iter 100 value 28.518464
## final value 28.518464
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 860.016269
## iter 10 value 145.679755
## iter 20 value 77.670248
## iter 30 value 45.697948
## iter 40 value 26.523448
## iter 50 value 20.283752
## iter 60 value 18.085218
## iter 70 value 16.554127
## iter 80 value 15.896341
## iter 90 value 15.127953
## iter 100 value 10.495466
## final value 10.495466
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 698.936853
## iter 10 value 100.968499
## iter 20 value 62.407217
## iter 30 value 39.872716
## iter 40 value 31.857413
## iter 50 value 29.120518
## iter 60 value 27.648080
## iter 70 value 26.833835
## iter 80 value 24.979109
## iter 90 value 23.354350
## iter 100 value 22.691058
## final value 22.691058
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 851.828295
## iter 10 value 150.498814
## iter 20 value 83.622196
## iter 30 value 56.326510
## iter 40 value 46.106244
## iter 50 value 41.944302
## iter 60 value 39.895324
## iter 70 value 38.688031
## iter 80 value 37.707072
## iter 90 value 37.108843
## iter 100 value 36.763477
## final value 36.763477
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 2092.385443
## iter 10 value 167.558886
## iter 20 value 100.879300
## iter 30 value 76.360467
## iter 40 value 58.027397
## iter 50 value 42.177186
## iter 60 value 31.221391
## iter 70 value 26.636117
## iter 80 value 25.037079
## iter 90 value 23.215281
## iter 100 value 22.446532
## final value 22.446532
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1228.924987
## iter 10 value 85.178115
## iter 20 value 49.602292
## iter 30 value 29.146849
## iter 40 value 20.190281
## iter 50 value 16.460944
## iter 60 value 8.120939
## iter 70 value 6.770546
## iter 80 value 6.370322
## iter 90 value 5.887599
## iter 100 value 5.651198
## final value 5.651198
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 869.328511
## iter 10 value 109.963543
## iter 20 value 79.203970
## iter 30 value 54.582756
## iter 40 value 33.993258
## iter 50 value 19.745597
## iter 60 value 15.601872
## iter 70 value 14.672381
## iter 80 value 13.884752
## iter 90 value 12.302464
## iter 100 value 11.286197
## final value 11.286197
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 789.998014
## iter 10 value 89.444986
## iter 20 value 49.570088
## iter 30 value 35.389270
## iter 40 value 26.113648
## iter 50 value 20.708830
## iter 60 value 18.942385
## iter 70 value 17.800696
## iter 80 value 17.385608
## iter 90 value 16.649574
## iter 100 value 15.744861
## final value 15.744861
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 635.278448
## iter 10 value 120.882600
## iter 20 value 71.655607
## iter 30 value 54.761864
## iter 40 value 44.002860
## iter 50 value 38.964080
## iter 60 value 35.999156
## iter 70 value 34.889803
## iter 80 value 34.275483
## iter 90 value 33.092560
## iter 100 value 32.222294
## final value 32.222294
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 612.391011
## iter 10 value 107.606703
## iter 20 value 55.228656
## iter 30 value 33.454314
## iter 40 value 22.498684
## iter 50 value 18.604124
## iter 60 value 16.840289
## iter 70 value 14.475960
## iter 80 value 13.370640
## iter 90 value 11.006865
## iter 100 value 9.086214
## final value 9.086214
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1072.892713
## iter 10 value 129.538809
## iter 20 value 88.495833
## iter 30 value 66.684826
## iter 40 value 56.320553
## iter 50 value 48.891576
## iter 60 value 42.619101
## iter 70 value 38.637188
## iter 80 value 34.769146
## iter 90 value 33.492439
## iter 100 value 32.712303
## final value 32.712303
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1081.565080
## iter 10 value 173.187093
## iter 20 value 108.870143
## iter 30 value 77.582238
## iter 40 value 61.970684
## iter 50 value 53.940781
## iter 60 value 48.216406
## iter 70 value 46.544279
## iter 80 value 44.975749
## iter 90 value 43.201511
## iter 100 value 42.421860
## final value 42.421860
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 743.643944
## iter 10 value 123.620134
## iter 20 value 75.941257
## iter 30 value 49.410853
## iter 40 value 41.609866
## iter 50 value 35.672965
## iter 60 value 28.689463
## iter 70 value 26.939983
## iter 80 value 25.179475
## iter 90 value 24.811607
## iter 100 value 24.622894
## final value 24.622894
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 988.789466
## iter 10 value 99.983463
## iter 20 value 51.702645
## iter 30 value 33.824345
## iter 40 value 25.355096
## iter 50 value 22.841275
## iter 60 value 21.161674
## iter 70 value 19.936568
## iter 80 value 18.146773
## iter 90 value 16.563985
## iter 100 value 15.636602
## final value 15.636602
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 714.491959
## iter 10 value 150.636664
## iter 20 value 81.378114
## iter 30 value 65.007160
## iter 40 value 61.627349
## iter 50 value 56.211447
## iter 60 value 53.031447
## iter 70 value 51.141709
## iter 80 value 49.200515
## iter 90 value 42.615959
## iter 100 value 39.117740
## final value 39.117740
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 920.517243
## iter 10 value 96.995096
## iter 20 value 56.646709
## iter 30 value 36.184907
## iter 40 value 25.488053
## iter 50 value 20.737430
## iter 60 value 17.859962
## iter 70 value 16.753466
## iter 80 value 16.144359
## iter 90 value 15.581397
## iter 100 value 15.300634
## final value 15.300634
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1192.896468
## iter 10 value 141.731331
## iter 20 value 86.409191
## iter 30 value 62.770632
## iter 40 value 53.192199
## iter 50 value 46.227808
## iter 60 value 44.997631
## iter 70 value 44.328236
## iter 80 value 42.150693
## iter 90 value 41.483238
## iter 100 value 39.183053
## final value 39.183053
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1034.489009
## iter 10 value 94.141685
## iter 20 value 49.377146
## iter 30 value 37.291416
## iter 40 value 29.669647
## iter 50 value 27.376815
## iter 60 value 26.428703
## iter 70 value 26.150454
## iter 80 value 25.598177
## iter 90 value 24.960924
## iter 100 value 24.757509
## final value 24.757509
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 727.452052
## iter 10 value 104.069020
## iter 20 value 68.866751
## iter 30 value 52.614142
## iter 40 value 45.853369
## iter 50 value 39.102741
## iter 60 value 36.030594
## iter 70 value 35.093639
## iter 80 value 34.271340
## iter 90 value 32.055619
## iter 100 value 30.791856
## final value 30.791856
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 886.515142
## iter 10 value 104.120498
## iter 20 value 58.769857
## iter 30 value 40.088675
## iter 40 value 31.007802
## iter 50 value 26.097740
## iter 60 value 23.938573
## iter 70 value 21.892416
## iter 80 value 19.884201
## iter 90 value 17.209500
## iter 100 value 15.163245
## final value 15.163245
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 670.938056
## iter 10 value 114.454299
## iter 20 value 64.969495
## iter 30 value 40.452766
## iter 40 value 21.776160
## iter 50 value 15.801758
## iter 60 value 13.546994
## iter 70 value 12.440615
## iter 80 value 11.558910
## iter 90 value 9.766393
## iter 100 value 8.279670
## final value 8.279670
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1072.802619
## iter 10 value 96.603928
## iter 20 value 58.637133
## iter 30 value 37.681506
## iter 40 value 26.194665
## iter 50 value 22.804843
## iter 60 value 19.706216
## iter 70 value 17.269985
## iter 80 value 16.374900
## iter 90 value 15.739147
## iter 100 value 15.271339
## final value 15.271339
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 773.378604
## iter 10 value 109.632232
## iter 20 value 72.326257
## iter 30 value 42.408020
## iter 40 value 22.424320
## iter 50 value 17.314526
## iter 60 value 15.924501
## iter 70 value 14.846049
## iter 80 value 13.988408
## iter 90 value 13.393379
## iter 100 value 12.986404
## final value 12.986404
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 751.720234
## iter 10 value 110.043695
## iter 20 value 76.705716
## iter 30 value 29.731784
## iter 40 value 15.338498
## iter 50 value 11.916016
## iter 60 value 10.701987
## iter 70 value 9.637492
## iter 80 value 8.864219
## iter 90 value 8.306553
## iter 100 value 7.723524
## final value 7.723524
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 954.106336
## iter 10 value 150.857363
## iter 20 value 92.233952
## iter 30 value 54.765157
## iter 40 value 35.906675
## iter 50 value 24.915539
## iter 60 value 21.675732
## iter 70 value 19.767580
## iter 80 value 17.459531
## iter 90 value 16.406650
## iter 100 value 14.936460
## final value 14.936460
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 760.010408
## iter 10 value 106.438478
## iter 20 value 52.619258
## iter 30 value 31.025301
## iter 40 value 17.797312
## iter 50 value 13.043937
## iter 60 value 11.905158
## iter 70 value 8.651584
## iter 80 value 7.959431
## iter 90 value 7.116555
## iter 100 value 6.567975
## final value 6.567975
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1037.129237
## iter 10 value 113.021875
## iter 20 value 60.007383
## iter 30 value 34.767594
## iter 40 value 21.229064
## iter 50 value 13.108175
## iter 60 value 11.320932
## iter 70 value 10.615131
## iter 80 value 10.127448
## iter 90 value 9.616795
## iter 100 value 9.375701
## final value 9.375701
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 783.373387
## iter 10 value 117.701599
## iter 20 value 64.549780
## iter 30 value 34.890939
## iter 40 value 21.737646
## iter 50 value 19.186548
## iter 60 value 16.845404
## iter 70 value 14.835302
## iter 80 value 13.596191
## iter 90 value 11.614323
## iter 100 value 9.992668
## final value 9.992668
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1333.706881
## iter 10 value 138.722775
## iter 20 value 92.484012
## iter 30 value 68.145282
## iter 40 value 45.989597
## iter 50 value 29.769829
## iter 60 value 20.020019
## iter 70 value 14.922119
## iter 80 value 13.324450
## iter 90 value 11.985143
## iter 100 value 11.179102
## final value 11.179102
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1011.953538
## iter 10 value 128.713270
## iter 20 value 87.650425
## iter 30 value 56.753691
## iter 40 value 38.595661
## iter 50 value 30.899842
## iter 60 value 28.702857
## iter 70 value 26.471884
## iter 80 value 24.680239
## iter 90 value 23.046147
## iter 100 value 22.199624
## final value 22.199624
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1084.072108
## iter 10 value 176.687288
## iter 20 value 99.941228
## iter 30 value 73.917753
## iter 40 value 48.754845
## iter 50 value 34.753308
## iter 60 value 23.518233
## iter 70 value 16.978114
## iter 80 value 14.121290
## iter 90 value 13.055412
## iter 100 value 12.078578
## final value 12.078578
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 754.582674
## iter 10 value 109.176795
## iter 20 value 71.474666
## iter 30 value 36.022652
## iter 40 value 23.530020
## iter 50 value 17.631559
## iter 60 value 16.481181
## iter 70 value 15.045626
## iter 80 value 13.399574
## iter 90 value 12.679408
## iter 100 value 11.981917
## final value 11.981917
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 902.265141
## iter 10 value 117.104768
## iter 20 value 76.156891
## iter 30 value 54.286808
## iter 40 value 42.014514
## iter 50 value 38.576146
## iter 60 value 33.757208
## iter 70 value 28.948178
## iter 80 value 26.440943
## iter 90 value 22.405969
## iter 100 value 19.270105
## final value 19.270105
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1054.855003
## iter 10 value 125.766035
## iter 20 value 82.885786
## iter 30 value 61.103378
## iter 40 value 44.208154
## iter 50 value 35.634222
## iter 60 value 30.189542
## iter 70 value 25.295118
## iter 80 value 23.619924
## iter 90 value 22.518636
## iter 100 value 20.479690
## final value 20.479690
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 902.717893
## iter 10 value 136.309277
## iter 20 value 90.240364
## iter 30 value 58.081834
## iter 40 value 43.895147
## iter 50 value 34.428651
## iter 60 value 28.066273
## iter 70 value 23.690617
## iter 80 value 21.254725
## iter 90 value 19.720396
## iter 100 value 18.698074
## final value 18.698074
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 916.690192
## iter 10 value 121.698104
## iter 20 value 72.619332
## iter 30 value 58.799122
## iter 40 value 50.276537
## iter 50 value 45.751423
## iter 60 value 40.823529
## iter 70 value 37.602293
## iter 80 value 34.876944
## iter 90 value 33.131314
## iter 100 value 31.660954
## final value 31.660954
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 964.895633
## iter 10 value 106.611654
## iter 20 value 85.244458
## iter 30 value 60.905401
## iter 40 value 40.058865
## iter 50 value 34.283580
## iter 60 value 33.062116
## iter 70 value 32.169929
## iter 80 value 31.447254
## iter 90 value 31.141419
## iter 100 value 30.738030
## final value 30.738030
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 752.381702
## iter 10 value 99.244651
## iter 20 value 68.748262
## iter 30 value 47.012342
## iter 40 value 34.230097
## iter 50 value 28.026791
## iter 60 value 25.816259
## iter 70 value 22.014581
## iter 80 value 18.266715
## iter 90 value 16.888009
## iter 100 value 15.844176
## final value 15.844176
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1258.191345
## iter 10 value 150.511712
## iter 20 value 94.331093
## iter 30 value 71.748477
## iter 40 value 62.408509
## iter 50 value 53.014862
## iter 60 value 47.834497
## iter 70 value 44.857315
## iter 80 value 42.829384
## iter 90 value 39.308058
## iter 100 value 37.228979
## final value 37.228979
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 898.641686
## iter 10 value 102.487546
## iter 20 value 71.736480
## iter 30 value 37.044427
## iter 40 value 22.659463
## iter 50 value 17.140177
## iter 60 value 15.936979
## iter 70 value 15.496508
## iter 80 value 15.101374
## iter 90 value 14.848090
## iter 100 value 13.780362
## final value 13.780362
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 952.085910
## iter 10 value 164.096544
## iter 20 value 124.016153
## iter 30 value 97.797020
## iter 40 value 79.470594
## iter 50 value 69.268924
## iter 60 value 61.006596
## iter 70 value 53.643199
## iter 80 value 47.406315
## iter 90 value 44.094019
## iter 100 value 42.962169
## final value 42.962169
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1302.389206
## iter 10 value 127.764523
## iter 20 value 64.320551
## iter 30 value 33.145037
## iter 40 value 20.671370
## iter 50 value 15.673082
## iter 60 value 12.943873
## iter 70 value 10.275648
## iter 80 value 7.277816
## iter 90 value 4.930378
## iter 100 value 4.036511
## final value 4.036511
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 583.055291
## iter 10 value 154.585478
## iter 20 value 78.966740
## iter 30 value 54.083050
## iter 40 value 43.667301
## iter 50 value 38.156206
## iter 60 value 35.118465
## iter 70 value 33.652586
## iter 80 value 32.452646
## iter 90 value 31.006417
## iter 100 value 30.453666
## final value 30.453666
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 663.474614
## iter 10 value 142.697585
## iter 20 value 85.154749
## iter 30 value 53.477575
## iter 40 value 38.428292
## iter 50 value 28.775161
## iter 60 value 25.484140
## iter 70 value 22.825050
## iter 80 value 19.902972
## iter 90 value 18.732300
## iter 100 value 17.745207
## final value 17.745207
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1023.047758
## iter 10 value 145.963766
## iter 20 value 60.132909
## iter 30 value 50.263386
## iter 40 value 46.989503
## iter 50 value 43.183065
## iter 60 value 39.460544
## iter 70 value 36.596356
## iter 80 value 34.679832
## iter 90 value 33.227983
## iter 100 value 30.267226
## final value 30.267226
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 952.046084
## iter 10 value 125.063727
## iter 20 value 71.964821
## iter 30 value 48.558642
## iter 40 value 41.013971
## iter 50 value 34.727686
## iter 60 value 31.388954
## iter 70 value 30.446693
## iter 80 value 29.611571
## iter 90 value 28.025810
## iter 100 value 27.042413
## final value 27.042413
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1183.436509
## iter 10 value 131.741609
## iter 20 value 82.692421
## iter 30 value 53.602395
## iter 40 value 30.711527
## iter 50 value 23.132727
## iter 60 value 15.728025
## iter 70 value 13.765730
## iter 80 value 10.938157
## iter 90 value 9.789564
## iter 100 value 8.894300
## final value 8.894300
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1016.891085
## iter 10 value 119.448016
## iter 20 value 78.475565
## iter 30 value 41.732168
## iter 40 value 23.296871
## iter 50 value 18.534543
## iter 60 value 17.250475
## iter 70 value 16.256496
## iter 80 value 14.650397
## iter 90 value 13.404327
## iter 100 value 12.816380
## final value 12.816380
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 646.126152
## iter 10 value 86.942899
## iter 20 value 53.207492
## iter 30 value 29.167207
## iter 40 value 22.025149
## iter 50 value 20.870664
## iter 60 value 20.368303
## iter 70 value 20.003486
## iter 80 value 19.699779
## iter 90 value 19.422933
## iter 100 value 18.997549
## final value 18.997549
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1582.283995
## iter 10 value 113.548862
## iter 20 value 72.790220
## iter 30 value 46.150549
## iter 40 value 31.462076
## iter 50 value 20.917588
## iter 60 value 17.905803
## iter 70 value 15.951484
## iter 80 value 14.817623
## iter 90 value 13.910068
## iter 100 value 13.093907
## final value 13.093907
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 727.049336
## iter 10 value 121.279500
## iter 20 value 80.517824
## iter 30 value 52.243709
## iter 40 value 32.079452
## iter 50 value 23.943246
## iter 60 value 22.615879
## iter 70 value 21.419910
## iter 80 value 20.750071
## iter 90 value 18.915077
## iter 100 value 18.183069
## final value 18.183069
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1300.940199
## iter 10 value 131.286156
## iter 20 value 82.259029
## iter 30 value 56.940543
## iter 40 value 36.481342
## iter 50 value 25.903445
## iter 60 value 16.526503
## iter 70 value 13.775819
## iter 80 value 10.436062
## iter 90 value 9.014255
## iter 100 value 8.399541
## final value 8.399541
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1240.149234
## iter 10 value 173.570682
## iter 20 value 109.441434
## iter 30 value 90.531218
## iter 40 value 66.181428
## iter 50 value 54.599363
## iter 60 value 48.084035
## iter 70 value 44.039228
## iter 80 value 41.814519
## iter 90 value 39.904137
## iter 100 value 38.487606
## final value 38.487606
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1246.201196
## iter 10 value 121.542138
## iter 20 value 82.181344
## iter 30 value 68.017670
## iter 40 value 54.690247
## iter 50 value 47.203375
## iter 60 value 45.234047
## iter 70 value 43.964907
## iter 80 value 41.752586
## iter 90 value 39.883296
## iter 100 value 38.192099
## final value 38.192099
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1441.547904
## iter 10 value 142.528036
## iter 20 value 98.228962
## iter 30 value 64.184387
## iter 40 value 42.556055
## iter 50 value 20.761241
## iter 60 value 12.189263
## iter 70 value 9.184198
## iter 80 value 7.453496
## iter 90 value 6.494017
## iter 100 value 6.138853
## final value 6.138853
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1187.932612
## iter 10 value 116.201117
## iter 20 value 64.916926
## iter 30 value 42.003614
## iter 40 value 22.620622
## iter 50 value 19.786402
## iter 60 value 18.725443
## iter 70 value 18.179109
## iter 80 value 17.833247
## iter 90 value 14.515587
## iter 100 value 12.886338
## final value 12.886338
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1723.504190
## iter 10 value 104.522189
## iter 20 value 66.306371
## iter 30 value 38.224853
## iter 40 value 30.238482
## iter 50 value 27.173298
## iter 60 value 25.403374
## iter 70 value 22.950287
## iter 80 value 22.541265
## iter 90 value 22.222791
## iter 100 value 21.534085
## final value 21.534085
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1036.601799
## iter 10 value 92.963684
## iter 20 value 60.632959
## iter 30 value 32.799611
## iter 40 value 18.863264
## iter 50 value 15.136852
## iter 60 value 13.843870
## iter 70 value 13.313945
## iter 80 value 13.028775
## iter 90 value 12.817327
## iter 100 value 12.728948
## final value 12.728948
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1308.225075
## iter 10 value 171.921228
## iter 20 value 107.709174
## iter 30 value 83.588807
## iter 40 value 69.595335
## iter 50 value 53.303152
## iter 60 value 45.059638
## iter 70 value 37.187075
## iter 80 value 32.302497
## iter 90 value 29.355884
## iter 100 value 28.144072
## final value 28.144072
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1177.581773
## iter 10 value 122.968195
## iter 20 value 67.790671
## iter 30 value 49.267191
## iter 40 value 41.330666
## iter 50 value 36.990779
## iter 60 value 34.595856
## iter 70 value 33.330627
## iter 80 value 31.739790
## iter 90 value 29.656407
## iter 100 value 28.380152
## final value 28.380152
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 658.243303
## iter 10 value 95.493852
## iter 20 value 67.574690
## iter 30 value 44.979731
## iter 40 value 35.913839
## iter 50 value 32.922822
## iter 60 value 30.097204
## iter 70 value 28.728245
## iter 80 value 27.712424
## iter 90 value 26.664873
## iter 100 value 26.280708
## final value 26.280708
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1052.177279
## iter 10 value 108.937687
## iter 20 value 68.046494
## iter 30 value 45.000628
## iter 40 value 36.587075
## iter 50 value 32.366837
## iter 60 value 29.280544
## iter 70 value 27.109424
## iter 80 value 26.039078
## iter 90 value 25.085060
## iter 100 value 22.131925
## final value 22.131925
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 886.967737
## iter 10 value 108.086771
## iter 20 value 73.787438
## iter 30 value 56.343122
## iter 40 value 48.486131
## iter 50 value 43.067322
## iter 60 value 40.915868
## iter 70 value 36.774763
## iter 80 value 33.224767
## iter 90 value 30.090174
## iter 100 value 27.953948
## final value 27.953948
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 678.450142
## iter 10 value 98.501976
## iter 20 value 58.470880
## iter 30 value 32.329661
## iter 40 value 16.544653
## iter 50 value 12.992758
## iter 60 value 11.476906
## iter 70 value 9.667768
## iter 80 value 8.697852
## iter 90 value 8.201432
## iter 100 value 8.009683
## final value 8.009683
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1322.037794
## iter 10 value 159.553359
## iter 20 value 113.903287
## iter 30 value 80.939239
## iter 40 value 56.289757
## iter 50 value 46.942378
## iter 60 value 43.220430
## iter 70 value 40.576494
## iter 80 value 37.953481
## iter 90 value 37.221041
## iter 100 value 36.174173
## final value 36.174173
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 880.155879
## iter 10 value 147.913959
## iter 20 value 112.095809
## iter 30 value 93.046482
## iter 40 value 79.489243
## iter 50 value 75.082918
## iter 60 value 69.841631
## iter 70 value 65.131151
## iter 80 value 63.520451
## iter 90 value 62.013485
## iter 100 value 61.112725
## final value 61.112725
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 657.971505
## iter 10 value 129.243874
## iter 20 value 89.834363
## iter 30 value 64.163624
## iter 40 value 32.216601
## iter 50 value 20.266372
## iter 60 value 17.061823
## iter 70 value 15.871024
## iter 80 value 15.161973
## iter 90 value 13.711989
## iter 100 value 12.560960
## final value 12.560960
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 966.706656
## iter 10 value 134.763633
## iter 20 value 96.386360
## iter 30 value 71.077871
## iter 40 value 59.516224
## iter 50 value 54.074357
## iter 60 value 51.198647
## iter 70 value 48.958223
## iter 80 value 47.186068
## iter 90 value 46.000071
## iter 100 value 43.283079
## final value 43.283079
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1074.924645
## iter 10 value 119.831360
## iter 20 value 79.899708
## iter 30 value 49.934371
## iter 40 value 35.955252
## iter 50 value 28.625384
## iter 60 value 22.355681
## iter 70 value 17.939550
## iter 80 value 16.646650
## iter 90 value 16.099093
## iter 100 value 15.759590
## final value 15.759590
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1301.393646
## iter 10 value 102.162824
## iter 20 value 61.164923
## iter 30 value 36.579365
## iter 40 value 23.291447
## iter 50 value 20.025014
## iter 60 value 19.205744
## iter 70 value 18.718706
## iter 80 value 18.375260
## iter 90 value 18.060334
## iter 100 value 17.625453
## final value 17.625453
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1116.013984
## iter 10 value 173.485159
## iter 20 value 107.133271
## iter 30 value 87.773562
## iter 40 value 76.505615
## iter 50 value 74.150877
## iter 60 value 71.964304
## iter 70 value 69.597838
## iter 80 value 68.628793
## iter 90 value 67.135874
## iter 100 value 65.309375
## final value 65.309375
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 622.654992
## iter 10 value 103.512679
## iter 20 value 67.935740
## iter 30 value 40.658400
## iter 40 value 30.569564
## iter 50 value 27.533643
## iter 60 value 26.367998
## iter 70 value 25.176183
## iter 80 value 24.089319
## iter 90 value 23.117741
## iter 100 value 22.788131
## final value 22.788131
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 958.708764
## iter 10 value 126.626833
## iter 20 value 61.772677
## iter 30 value 48.037003
## iter 40 value 42.893765
## iter 50 value 41.558365
## iter 60 value 39.657384
## iter 70 value 39.012518
## iter 80 value 38.589229
## iter 90 value 38.233166
## iter 100 value 37.126494
## final value 37.126494
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1447.118179
## iter 10 value 105.404476
## iter 20 value 70.565630
## iter 30 value 43.823166
## iter 40 value 35.079124
## iter 50 value 29.072285
## iter 60 value 27.838233
## iter 70 value 26.953312
## iter 80 value 26.088201
## iter 90 value 25.297110
## iter 100 value 22.837699
## final value 22.837699
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 986.906288
## iter 10 value 114.909388
## iter 20 value 76.340104
## iter 30 value 53.473613
## iter 40 value 31.959055
## iter 50 value 15.331188
## iter 60 value 8.766322
## iter 70 value 6.453551
## iter 80 value 5.624861
## iter 90 value 4.943016
## iter 100 value 4.492577
## final value 4.492577
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 762.837202
## iter 10 value 131.440839
## iter 20 value 78.347263
## iter 30 value 54.408775
## iter 40 value 38.921015
## iter 50 value 35.610960
## iter 60 value 34.078641
## iter 70 value 31.768408
## iter 80 value 30.380991
## iter 90 value 29.488631
## iter 100 value 28.096294
## final value 28.096294
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1670.103243
## iter 10 value 134.057537
## iter 20 value 97.986434
## iter 30 value 75.746558
## iter 40 value 62.774028
## iter 50 value 52.381660
## iter 60 value 47.362211
## iter 70 value 42.887415
## iter 80 value 41.487605
## iter 90 value 38.618833
## iter 100 value 35.487568
## final value 35.487568
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1067.057239
## iter 10 value 151.376577
## iter 20 value 89.643678
## iter 30 value 64.338365
## iter 40 value 49.684368
## iter 50 value 43.973080
## iter 60 value 41.415160
## iter 70 value 39.921308
## iter 80 value 38.830269
## iter 90 value 37.802911
## iter 100 value 37.351517
## final value 37.351517
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 876.035778
## iter 10 value 113.211449
## iter 20 value 65.940480
## iter 30 value 37.450190
## iter 40 value 22.254023
## iter 50 value 19.776181
## iter 60 value 18.265624
## iter 70 value 17.268010
## iter 80 value 16.707618
## iter 90 value 16.286017
## iter 100 value 15.773495
## final value 15.773495
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 973.320352
## iter 10 value 128.956373
## iter 20 value 97.250805
## iter 30 value 77.155919
## iter 40 value 64.978701
## iter 50 value 59.409812
## iter 60 value 55.838887
## iter 70 value 53.188994
## iter 80 value 49.899683
## iter 90 value 47.761841
## iter 100 value 46.055060
## final value 46.055060
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 581.933434
## iter 10 value 108.071319
## iter 20 value 74.292035
## iter 30 value 57.404394
## iter 40 value 39.848862
## iter 50 value 32.497520
## iter 60 value 30.402098
## iter 70 value 28.776759
## iter 80 value 26.952412
## iter 90 value 25.259809
## iter 100 value 23.649393
## final value 23.649393
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 736.393933
## iter 10 value 101.315317
## iter 20 value 59.027277
## iter 30 value 36.236675
## iter 40 value 24.023246
## iter 50 value 21.914687
## iter 60 value 21.061347
## iter 70 value 19.832100
## iter 80 value 18.781146
## iter 90 value 18.227434
## iter 100 value 17.607662
## final value 17.607662
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1507.194092
## iter 10 value 156.754529
## iter 20 value 100.353689
## iter 30 value 82.813168
## iter 40 value 61.572796
## iter 50 value 48.900051
## iter 60 value 43.405475
## iter 70 value 39.910654
## iter 80 value 38.218925
## iter 90 value 37.352326
## iter 100 value 36.721534
## final value 36.721534
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 749.160031
## iter 10 value 103.303548
## iter 20 value 50.269006
## iter 30 value 19.960822
## iter 40 value 12.559027
## iter 50 value 10.394599
## iter 60 value 8.023343
## iter 70 value 4.261491
## iter 80 value 3.380974
## iter 90 value 2.978823
## iter 100 value 2.641745
## final value 2.641745
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1007.183742
## iter 10 value 94.882244
## iter 20 value 57.616501
## iter 30 value 37.861314
## iter 40 value 18.234199
## iter 50 value 13.122146
## iter 60 value 11.359597
## iter 70 value 9.490115
## iter 80 value 8.433435
## iter 90 value 7.864330
## iter 100 value 7.431229
## final value 7.431229
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 838.448811
## iter 10 value 113.236945
## iter 20 value 64.267963
## iter 30 value 36.953424
## iter 40 value 22.430448
## iter 50 value 15.842709
## iter 60 value 12.109406
## iter 70 value 9.376086
## iter 80 value 8.821272
## iter 90 value 8.472459
## iter 100 value 8.307185
## final value 8.307185
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 919.911434
## iter 10 value 119.401038
## iter 20 value 74.124010
## iter 30 value 50.428999
## iter 40 value 35.513102
## iter 50 value 30.411000
## iter 60 value 22.476228
## iter 70 value 17.925414
## iter 80 value 15.893892
## iter 90 value 14.647302
## iter 100 value 13.773476
## final value 13.773476
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1074.140422
## iter 10 value 150.832903
## iter 20 value 87.718032
## iter 30 value 63.323145
## iter 40 value 54.479646
## iter 50 value 52.416131
## iter 60 value 51.048677
## iter 70 value 46.356073
## iter 80 value 45.630798
## iter 90 value 41.904856
## iter 100 value 39.971485
## final value 39.971485
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1264.398826
## iter 10 value 132.528423
## iter 20 value 77.080745
## iter 30 value 54.960494
## iter 40 value 45.989496
## iter 50 value 43.495586
## iter 60 value 42.853533
## iter 70 value 42.090077
## iter 80 value 41.477755
## iter 90 value 40.563421
## iter 100 value 39.522679
## final value 39.522679
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1233.877787
## iter 10 value 157.268376
## iter 20 value 93.812835
## iter 30 value 57.635528
## iter 40 value 38.329201
## iter 50 value 34.452157
## iter 60 value 31.159711
## iter 70 value 29.442957
## iter 80 value 28.036074
## iter 90 value 26.584490
## iter 100 value 24.472030
## final value 24.472030
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 803.901867
## iter 10 value 127.128175
## iter 20 value 79.458983
## iter 30 value 60.407971
## iter 40 value 43.541535
## iter 50 value 34.394286
## iter 60 value 30.383093
## iter 70 value 27.940053
## iter 80 value 24.517397
## iter 90 value 21.260865
## iter 100 value 20.686779
## final value 20.686779
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 637.093382
## iter 10 value 135.213543
## iter 20 value 55.114466
## iter 30 value 35.214824
## iter 40 value 29.979029
## iter 50 value 27.887886
## iter 60 value 26.187198
## iter 70 value 25.414628
## iter 80 value 24.890089
## iter 90 value 24.134694
## iter 100 value 21.906774
## final value 21.906774
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1124.179104
## iter 10 value 126.486587
## iter 20 value 66.062206
## iter 30 value 47.076737
## iter 40 value 34.540671
## iter 50 value 28.648815
## iter 60 value 26.835443
## iter 70 value 24.971805
## iter 80 value 23.603740
## iter 90 value 22.446222
## iter 100 value 21.309889
## final value 21.309889
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1048.785943
## iter 10 value 118.019049
## iter 20 value 73.229787
## iter 30 value 55.045021
## iter 40 value 44.192043
## iter 50 value 40.843984
## iter 60 value 39.369914
## iter 70 value 38.814363
## iter 80 value 38.411310
## iter 90 value 37.998364
## iter 100 value 37.854640
## final value 37.854640
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1117.551259
## iter 10 value 160.856041
## iter 20 value 108.342793
## iter 30 value 90.697886
## iter 40 value 82.243140
## iter 50 value 78.532443
## iter 60 value 76.323415
## iter 70 value 71.955475
## iter 80 value 68.834383
## iter 90 value 66.344544
## iter 100 value 64.825128
## final value 64.825128
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1006.347089
## iter 10 value 98.427079
## iter 20 value 65.461709
## iter 30 value 41.984287
## iter 40 value 32.620214
## iter 50 value 29.822886
## iter 60 value 28.731104
## iter 70 value 28.252177
## iter 80 value 27.951383
## iter 90 value 26.596132
## iter 100 value 24.788683
## final value 24.788683
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 712.888680
## iter 10 value 122.359195
## iter 20 value 82.473362
## iter 30 value 60.686189
## iter 40 value 51.371003
## iter 50 value 42.495191
## iter 60 value 37.187990
## iter 70 value 35.524217
## iter 80 value 34.113698
## iter 90 value 33.385242
## iter 100 value 32.770662
## final value 32.770662
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 973.709615
## iter 10 value 123.123248
## iter 20 value 68.629584
## iter 30 value 53.592225
## iter 40 value 41.989607
## iter 50 value 34.780545
## iter 60 value 28.454467
## iter 70 value 26.078283
## iter 80 value 25.203825
## iter 90 value 24.495854
## iter 100 value 24.139989
## final value 24.139989
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 730.885570
## iter 10 value 144.165372
## iter 20 value 53.777133
## iter 30 value 28.144509
## iter 40 value 16.428575
## iter 50 value 13.676046
## iter 60 value 12.587461
## iter 70 value 10.636616
## iter 80 value 8.731839
## iter 90 value 8.225439
## iter 100 value 6.457375
## final value 6.457375
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 705.208391
## iter 10 value 99.484632
## iter 20 value 78.643007
## iter 30 value 58.378434
## iter 40 value 43.368189
## iter 50 value 37.328547
## iter 60 value 30.210300
## iter 70 value 25.879215
## iter 80 value 24.160951
## iter 90 value 20.442843
## iter 100 value 19.134106
## final value 19.134106
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1229.197137
## iter 10 value 161.745222
## iter 20 value 116.686440
## iter 30 value 98.429882
## iter 40 value 87.445776
## iter 50 value 75.336979
## iter 60 value 62.759346
## iter 70 value 57.350810
## iter 80 value 51.898649
## iter 90 value 48.927635
## iter 100 value 46.814485
## final value 46.814485
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 686.323778
## iter 10 value 117.746585
## iter 20 value 83.223841
## iter 30 value 50.567364
## iter 40 value 36.381746
## iter 50 value 29.678476
## iter 60 value 26.840983
## iter 70 value 25.855474
## iter 80 value 24.767319
## iter 90 value 23.777623
## iter 100 value 23.325025
## final value 23.325025
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 873.077666
## iter 10 value 251.904247
## iter 20 value 205.821684
## iter 30 value 130.359894
## iter 40 value 88.689489
## iter 50 value 71.889794
## iter 60 value 67.616850
## iter 70 value 63.709439
## iter 80 value 61.523605
## iter 90 value 57.329331
## iter 100 value 52.773484
## final value 52.773484
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 629.574647
## iter 10 value 131.509578
## iter 20 value 84.265800
## iter 30 value 53.935444
## iter 40 value 41.622321
## iter 50 value 34.099153
## iter 60 value 28.411630
## iter 70 value 25.112422
## iter 80 value 23.883445
## iter 90 value 22.905615
## iter 100 value 22.080140
## final value 22.080140
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 849.909391
## iter 10 value 123.825976
## iter 20 value 93.739242
## iter 30 value 71.848938
## iter 40 value 56.871110
## iter 50 value 50.477434
## iter 60 value 46.126737
## iter 70 value 43.527939
## iter 80 value 39.365048
## iter 90 value 38.217009
## iter 100 value 36.541493
## final value 36.541493
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 665.280011
## iter 10 value 138.061393
## iter 20 value 98.322618
## iter 30 value 78.873853
## iter 40 value 59.587999
## iter 50 value 50.184220
## iter 60 value 45.304378
## iter 70 value 40.530694
## iter 80 value 36.792860
## iter 90 value 32.925392
## iter 100 value 31.466505
## final value 31.466505
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1026.463376
## iter 10 value 157.183380
## iter 20 value 116.773153
## iter 30 value 82.056269
## iter 40 value 65.256075
## iter 50 value 45.361886
## iter 60 value 37.103921
## iter 70 value 35.259690
## iter 80 value 33.455919
## iter 90 value 31.827247
## iter 100 value 30.794873
## final value 30.794873
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 613.463518
## iter 10 value 106.058588
## iter 20 value 61.003764
## iter 30 value 35.932843
## iter 40 value 21.853358
## iter 50 value 15.760834
## iter 60 value 14.544925
## iter 70 value 13.467181
## iter 80 value 12.690188
## iter 90 value 11.798217
## iter 100 value 11.067703
## final value 11.067703
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1093.137837
## iter 10 value 83.852904
## iter 20 value 35.072338
## iter 30 value 16.639335
## iter 40 value 12.923793
## iter 50 value 11.747341
## iter 60 value 11.301598
## iter 70 value 10.191396
## iter 80 value 8.557863
## iter 90 value 6.907521
## iter 100 value 5.792462
## final value 5.792462
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1186.883896
## iter 10 value 88.001749
## iter 20 value 51.452118
## iter 30 value 27.814892
## iter 40 value 19.065180
## iter 50 value 15.629486
## iter 60 value 14.921401
## iter 70 value 14.617823
## iter 80 value 14.391183
## iter 90 value 12.741627
## iter 100 value 12.079037
## final value 12.079037
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 776.431878
## iter 10 value 98.189762
## iter 20 value 63.414726
## iter 30 value 38.102220
## iter 40 value 27.849111
## iter 50 value 21.431707
## iter 60 value 17.790970
## iter 70 value 16.511732
## iter 80 value 15.708078
## iter 90 value 14.537453
## iter 100 value 14.101078
## final value 14.101078
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 884.593771
## iter 10 value 75.758289
## iter 20 value 45.231695
## iter 30 value 25.894201
## iter 40 value 13.305748
## iter 50 value 10.536901
## iter 60 value 9.575706
## iter 70 value 8.730783
## iter 80 value 8.203675
## iter 90 value 7.886139
## iter 100 value 7.673461
## final value 7.673461
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1221.493786
## iter 10 value 138.424485
## iter 20 value 84.160631
## iter 30 value 53.888254
## iter 40 value 33.888575
## iter 50 value 25.777309
## iter 60 value 23.412021
## iter 70 value 22.173367
## iter 80 value 20.897775
## iter 90 value 19.927815
## iter 100 value 15.775553
## final value 15.775553
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 649.352485
## iter 10 value 92.766063
## iter 20 value 60.106588
## iter 30 value 35.545316
## iter 40 value 29.768942
## iter 50 value 26.662276
## iter 60 value 24.603940
## iter 70 value 22.426071
## iter 80 value 21.836614
## iter 90 value 21.590443
## iter 100 value 21.412941
## final value 21.412941
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1300.371430
## iter 10 value 106.754346
## iter 20 value 60.832573
## iter 30 value 41.366622
## iter 40 value 30.071765
## iter 50 value 24.595219
## iter 60 value 16.053617
## iter 70 value 11.038256
## iter 80 value 10.269525
## iter 90 value 9.870124
## iter 100 value 9.489435
## final value 9.489435
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 622.415982
## iter 10 value 83.867198
## iter 20 value 49.294275
## iter 30 value 36.892249
## iter 40 value 25.852047
## iter 50 value 18.786648
## iter 60 value 17.324171
## iter 70 value 16.838201
## iter 80 value 16.389900
## iter 90 value 16.026828
## iter 100 value 15.640544
## final value 15.640544
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1388.276788
## iter 10 value 90.227970
## iter 20 value 59.307349
## iter 30 value 31.551268
## iter 40 value 14.819713
## iter 50 value 8.400953
## iter 60 value 5.810941
## iter 70 value 5.141554
## iter 80 value 4.642290
## iter 90 value 4.222428
## iter 100 value 4.039826
## final value 4.039826
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1341.812895
## iter 10 value 102.620850
## iter 20 value 53.117274
## iter 30 value 33.477437
## iter 40 value 26.807496
## iter 50 value 23.834630
## iter 60 value 20.471421
## iter 70 value 16.468304
## iter 80 value 15.490810
## iter 90 value 14.745578
## iter 100 value 12.327694
## final value 12.327694
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1428.061525
## iter 10 value 98.080502
## iter 20 value 62.034888
## iter 30 value 42.175145
## iter 40 value 32.008377
## iter 50 value 26.322553
## iter 60 value 24.659389
## iter 70 value 22.581619
## iter 80 value 22.131209
## iter 90 value 21.639036
## iter 100 value 19.887164
## final value 19.887164
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 697.453652
## iter 10 value 129.317034
## iter 20 value 85.804158
## iter 30 value 57.523674
## iter 40 value 48.070305
## iter 50 value 43.141697
## iter 60 value 40.406987
## iter 70 value 38.095786
## iter 80 value 35.223600
## iter 90 value 32.914374
## iter 100 value 31.102705
## final value 31.102705
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1047.400779
## iter 10 value 110.626107
## iter 20 value 61.660980
## iter 30 value 36.885126
## iter 40 value 16.408272
## iter 50 value 10.214304
## iter 60 value 8.394365
## iter 70 value 7.659440
## iter 80 value 6.907002
## iter 90 value 6.568888
## iter 100 value 6.308883
## final value 6.308883
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 882.143778
## iter 10 value 129.392034
## iter 20 value 92.788120
## iter 30 value 65.588992
## iter 40 value 50.167884
## iter 50 value 45.669178
## iter 60 value 44.276873
## iter 70 value 42.619741
## iter 80 value 41.158201
## iter 90 value 39.348750
## iter 100 value 34.523313
## final value 34.523313
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1792.120670
## iter 10 value 128.842807
## iter 20 value 74.051793
## iter 30 value 53.692603
## iter 40 value 37.728050
## iter 50 value 29.161711
## iter 60 value 24.123147
## iter 70 value 22.151510
## iter 80 value 20.158639
## iter 90 value 18.800232
## iter 100 value 17.667960
## final value 17.667960
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1054.502002
## iter 10 value 132.290711
## iter 20 value 61.776124
## iter 30 value 34.716406
## iter 40 value 18.917255
## iter 50 value 11.520558
## iter 60 value 8.056793
## iter 70 value 6.835824
## iter 80 value 6.396021
## iter 90 value 6.111974
## iter 100 value 5.737433
## final value 5.737433
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 881.345597
## iter 10 value 126.544856
## iter 20 value 73.616827
## iter 30 value 45.466230
## iter 40 value 35.595798
## iter 50 value 29.831187
## iter 60 value 28.076862
## iter 70 value 27.061126
## iter 80 value 26.217112
## iter 90 value 25.264435
## iter 100 value 24.581129
## final value 24.581129
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 749.306101
## iter 10 value 107.134771
## iter 20 value 67.709630
## iter 30 value 45.326114
## iter 40 value 36.102237
## iter 50 value 33.121811
## iter 60 value 29.308682
## iter 70 value 22.651062
## iter 80 value 20.092318
## iter 90 value 18.944342
## iter 100 value 17.826286
## final value 17.826286
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1158.575581
## iter 10 value 172.657914
## iter 20 value 102.337970
## iter 30 value 80.368028
## iter 40 value 61.059048
## iter 50 value 48.140863
## iter 60 value 37.942430
## iter 70 value 32.598648
## iter 80 value 29.350860
## iter 90 value 26.766053
## iter 100 value 25.609738
## final value 25.609738
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1632.704693
## iter 10 value 123.980119
## iter 20 value 60.002867
## iter 30 value 45.400155
## iter 40 value 32.025905
## iter 50 value 25.004500
## iter 60 value 21.423647
## iter 70 value 19.963737
## iter 80 value 18.467920
## iter 90 value 16.396914
## iter 100 value 15.718884
## final value 15.718884
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1028.394974
## iter 10 value 151.268022
## iter 20 value 100.105705
## iter 30 value 74.458310
## iter 40 value 60.312498
## iter 50 value 56.871453
## iter 60 value 51.475894
## iter 70 value 44.206816
## iter 80 value 38.122663
## iter 90 value 36.460322
## iter 100 value 34.374271
## final value 34.374271
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1099.421992
## iter 10 value 172.002809
## iter 20 value 106.989256
## iter 30 value 91.141799
## iter 40 value 77.554755
## iter 50 value 70.667446
## iter 60 value 67.895934
## iter 70 value 65.914131
## iter 80 value 60.409017
## iter 90 value 59.612858
## iter 100 value 59.052468
## final value 59.052468
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1659.565369
## iter 10 value 152.122793
## iter 20 value 104.241292
## iter 30 value 85.671362
## iter 40 value 71.274518
## iter 50 value 62.878175
## iter 60 value 56.316856
## iter 70 value 53.386421
## iter 80 value 51.815737
## iter 90 value 51.005989
## iter 100 value 50.354222
## final value 50.354222
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1117.873977
## iter 10 value 126.735013
## iter 20 value 91.708796
## iter 30 value 64.921087
## iter 40 value 51.564586
## iter 50 value 39.129304
## iter 60 value 33.390231
## iter 70 value 29.299760
## iter 80 value 25.001638
## iter 90 value 23.059061
## iter 100 value 22.067252
## final value 22.067252
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1045.750357
## iter 10 value 216.451799
## iter 20 value 97.620590
## iter 30 value 47.684501
## iter 40 value 29.093954
## iter 50 value 18.294517
## iter 60 value 14.374176
## iter 70 value 13.573437
## iter 80 value 13.011800
## iter 90 value 12.403657
## iter 100 value 11.706221
## final value 11.706221
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 784.325651
## iter 10 value 120.686497
## iter 20 value 64.307645
## iter 30 value 55.717435
## iter 40 value 46.092851
## iter 50 value 36.850424
## iter 60 value 31.881423
## iter 70 value 28.986970
## iter 80 value 26.924289
## iter 90 value 24.094264
## iter 100 value 21.240172
## final value 21.240172
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 834.710985
## iter 10 value 127.168822
## iter 20 value 85.184628
## iter 30 value 61.542006
## iter 40 value 45.856524
## iter 50 value 40.898109
## iter 60 value 38.480930
## iter 70 value 36.039392
## iter 80 value 34.151811
## iter 90 value 32.332708
## iter 100 value 31.752769
## final value 31.752769
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 664.449830
## iter 10 value 124.799228
## iter 20 value 77.723107
## iter 30 value 56.761253
## iter 40 value 30.644267
## iter 50 value 21.380753
## iter 60 value 18.937659
## iter 70 value 17.362614
## iter 80 value 16.852073
## iter 90 value 16.255578
## iter 100 value 15.937048
## final value 15.937048
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1592.161139
## iter 10 value 164.560348
## iter 20 value 104.717859
## iter 30 value 75.629775
## iter 40 value 60.603570
## iter 50 value 51.916972
## iter 60 value 45.118340
## iter 70 value 42.494433
## iter 80 value 41.767296
## iter 90 value 41.486750
## iter 100 value 41.370826
## final value 41.370826
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 658.840474
## iter 10 value 92.835016
## iter 20 value 57.967436
## iter 30 value 39.847065
## iter 40 value 28.541084
## iter 50 value 19.711331
## iter 60 value 18.196322
## iter 70 value 17.512934
## iter 80 value 17.208259
## iter 90 value 17.038975
## iter 100 value 16.863768
## final value 16.863768
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 740.790099
## iter 10 value 130.890688
## iter 20 value 79.985647
## iter 30 value 62.244400
## iter 40 value 56.740953
## iter 50 value 55.708515
## iter 60 value 54.136965
## iter 70 value 52.649451
## iter 80 value 50.413778
## iter 90 value 46.967873
## iter 100 value 46.382536
## final value 46.382536
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 661.710708
## iter 10 value 120.271986
## iter 20 value 77.893136
## iter 30 value 56.953546
## iter 40 value 42.383248
## iter 50 value 32.557554
## iter 60 value 27.393857
## iter 70 value 24.046864
## iter 80 value 18.932480
## iter 90 value 16.623584
## iter 100 value 15.376632
## final value 15.376632
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 737.601993
## iter 10 value 141.616238
## iter 20 value 71.575221
## iter 30 value 45.143917
## iter 40 value 26.726379
## iter 50 value 24.268134
## iter 60 value 22.734634
## iter 70 value 19.502081
## iter 80 value 18.438713
## iter 90 value 17.790368
## iter 100 value 17.246619
## final value 17.246619
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1014.605552
## iter 10 value 137.495706
## iter 20 value 69.929929
## iter 30 value 48.537727
## iter 40 value 33.688979
## iter 50 value 28.207427
## iter 60 value 24.156475
## iter 70 value 21.027962
## iter 80 value 15.055694
## iter 90 value 13.669044
## iter 100 value 13.149689
## final value 13.149689
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 871.014746
## iter 10 value 153.046248
## iter 20 value 103.329357
## iter 30 value 71.169208
## iter 40 value 52.067732
## iter 50 value 37.631827
## iter 60 value 28.452878
## iter 70 value 25.374398
## iter 80 value 21.904702
## iter 90 value 19.984635
## iter 100 value 18.828913
## final value 18.828913
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1231.712310
## iter 10 value 124.340879
## iter 20 value 79.174105
## iter 30 value 61.011413
## iter 40 value 46.341594
## iter 50 value 34.436822
## iter 60 value 29.885607
## iter 70 value 28.270969
## iter 80 value 26.776261
## iter 90 value 25.893673
## iter 100 value 25.297361
## final value 25.297361
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1067.732725
## iter 10 value 121.246722
## iter 20 value 86.363111
## iter 30 value 61.513014
## iter 40 value 52.115629
## iter 50 value 42.957900
## iter 60 value 40.823479
## iter 70 value 39.773968
## iter 80 value 38.647849
## iter 90 value 37.509098
## iter 100 value 36.529919
## final value 36.529919
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1198.722340
## iter 10 value 123.054966
## iter 20 value 76.034056
## iter 30 value 48.762422
## iter 40 value 33.329886
## iter 50 value 26.896495
## iter 60 value 24.595371
## iter 70 value 22.857625
## iter 80 value 22.009065
## iter 90 value 21.436909
## iter 100 value 20.769204
## final value 20.769204
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1411.452124
## iter 10 value 144.093852
## iter 20 value 88.983078
## iter 30 value 57.855651
## iter 40 value 43.138707
## iter 50 value 33.785251
## iter 60 value 28.841188
## iter 70 value 21.597457
## iter 80 value 18.384090
## iter 90 value 16.033748
## iter 100 value 14.288980
## final value 14.288980
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 864.691051
## iter 10 value 127.722056
## iter 20 value 92.104598
## iter 30 value 65.698924
## iter 40 value 47.711643
## iter 50 value 35.970903
## iter 60 value 28.994867
## iter 70 value 26.039643
## iter 80 value 24.357933
## iter 90 value 23.114614
## iter 100 value 20.505011
## final value 20.505011
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1398.414805
## iter 10 value 169.528108
## iter 20 value 116.338215
## iter 30 value 86.821256
## iter 40 value 58.546813
## iter 50 value 48.161525
## iter 60 value 45.327030
## iter 70 value 41.706457
## iter 80 value 39.701561
## iter 90 value 38.670141
## iter 100 value 36.139023
## final value 36.139023
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 728.908585
## iter 10 value 123.353744
## iter 20 value 79.581771
## iter 30 value 52.217499
## iter 40 value 39.798616
## iter 50 value 33.176839
## iter 60 value 30.031678
## iter 70 value 25.477489
## iter 80 value 24.346198
## iter 90 value 22.903675
## iter 100 value 22.439879
## final value 22.439879
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 942.162004
## iter 10 value 96.157048
## iter 20 value 66.482679
## iter 30 value 39.851736
## iter 40 value 24.092600
## iter 50 value 18.495097
## iter 60 value 16.481115
## iter 70 value 13.641759
## iter 80 value 13.149060
## iter 90 value 12.854124
## iter 100 value 12.565997
## final value 12.565997
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1229.928072
## iter 10 value 164.711354
## iter 20 value 89.963466
## iter 30 value 58.364064
## iter 40 value 40.427912
## iter 50 value 32.615547
## iter 60 value 30.988605
## iter 70 value 29.492948
## iter 80 value 28.473591
## iter 90 value 27.518254
## iter 100 value 26.578047
## final value 26.578047
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1223.600138
## iter 10 value 98.733896
## iter 20 value 66.693741
## iter 30 value 39.539255
## iter 40 value 28.170756
## iter 50 value 24.899689
## iter 60 value 16.961827
## iter 70 value 15.166721
## iter 80 value 13.744446
## iter 90 value 12.068941
## iter 100 value 11.567918
## final value 11.567918
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 796.157277
## iter 10 value 111.673348
## iter 20 value 67.424350
## iter 30 value 37.514418
## iter 40 value 28.710776
## iter 50 value 25.446332
## iter 60 value 21.252969
## iter 70 value 19.113033
## iter 80 value 18.253161
## iter 90 value 17.657057
## iter 100 value 13.356019
## final value 13.356019
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1314.868201
## iter 10 value 143.210820
## iter 20 value 85.321689
## iter 30 value 66.313973
## iter 40 value 58.187916
## iter 50 value 55.923846
## iter 60 value 55.255012
## iter 70 value 54.324853
## iter 80 value 50.239850
## iter 90 value 45.906059
## iter 100 value 44.204768
## final value 44.204768
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1010.179785
## iter 10 value 133.010432
## iter 20 value 89.344909
## iter 30 value 68.150519
## iter 40 value 62.556703
## iter 50 value 58.865548
## iter 60 value 55.622642
## iter 70 value 53.333750
## iter 80 value 52.339969
## iter 90 value 51.970811
## iter 100 value 45.405095
## final value 45.405095
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 712.812558
## iter 10 value 142.839054
## iter 20 value 102.689080
## iter 30 value 74.182589
## iter 40 value 53.475853
## iter 50 value 46.488098
## iter 60 value 43.250217
## iter 70 value 41.953726
## iter 80 value 40.953770
## iter 90 value 40.307168
## iter 100 value 39.706732
## final value 39.706732
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1362.144482
## iter 10 value 129.820071
## iter 20 value 97.703730
## iter 30 value 65.476447
## iter 40 value 48.226837
## iter 50 value 34.210065
## iter 60 value 30.546027
## iter 70 value 29.310343
## iter 80 value 28.539819
## iter 90 value 27.970128
## iter 100 value 26.707062
## final value 26.707062
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1647.066492
## iter 10 value 119.445229
## iter 20 value 74.570440
## iter 30 value 57.748380
## iter 40 value 51.070737
## iter 50 value 48.629215
## iter 60 value 46.901972
## iter 70 value 45.474681
## iter 80 value 44.048030
## iter 90 value 43.478117
## iter 100 value 43.184989
## final value 43.184989
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1072.690588
## iter 10 value 143.390131
## iter 20 value 83.539518
## iter 30 value 52.669284
## iter 40 value 41.588897
## iter 50 value 38.343004
## iter 60 value 35.338834
## iter 70 value 33.093810
## iter 80 value 32.059026
## iter 90 value 31.615300
## iter 100 value 31.460972
## final value 31.460972
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1115.722458
## iter 10 value 186.067705
## iter 20 value 136.822786
## iter 30 value 101.475055
## iter 40 value 80.888993
## iter 50 value 68.739762
## iter 60 value 63.201361
## iter 70 value 60.394465
## iter 80 value 56.926682
## iter 90 value 54.772279
## iter 100 value 53.259534
## final value 53.259534
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1347.348351
## iter 10 value 119.283538
## iter 20 value 75.391144
## iter 30 value 57.638563
## iter 40 value 41.046020
## iter 50 value 30.351843
## iter 60 value 18.921362
## iter 70 value 13.689574
## iter 80 value 6.287260
## iter 90 value 5.103203
## iter 100 value 4.508947
## final value 4.508947
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1637.699780
## iter 10 value 93.638853
## iter 20 value 42.007632
## iter 30 value 23.556351
## iter 40 value 19.289690
## iter 50 value 17.838540
## iter 60 value 16.600745
## iter 70 value 14.566365
## iter 80 value 13.493842
## iter 90 value 11.790590
## iter 100 value 10.801087
## final value 10.801087
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 728.407297
## iter 10 value 133.120478
## iter 20 value 73.401981
## iter 30 value 37.103038
## iter 40 value 30.198920
## iter 50 value 26.501968
## iter 60 value 22.614717
## iter 70 value 20.936010
## iter 80 value 19.739772
## iter 90 value 17.991408
## iter 100 value 17.153825
## final value 17.153825
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1660.138021
## iter 10 value 86.850475
## iter 20 value 40.131254
## iter 30 value 24.529881
## iter 40 value 19.719543
## iter 50 value 18.096355
## iter 60 value 14.372810
## iter 70 value 12.140374
## iter 80 value 11.799675
## iter 90 value 11.318708
## iter 100 value 10.892758
## final value 10.892758
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 871.461883
## iter 10 value 120.315946
## iter 20 value 72.137346
## iter 30 value 48.451205
## iter 40 value 38.348391
## iter 50 value 35.533473
## iter 60 value 34.116516
## iter 70 value 32.511124
## iter 80 value 31.569171
## iter 90 value 31.154358
## iter 100 value 30.677204
## final value 30.677204
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 846.047295
## iter 10 value 123.840820
## iter 20 value 90.880054
## iter 30 value 68.885604
## iter 40 value 43.112193
## iter 50 value 34.745372
## iter 60 value 30.508083
## iter 70 value 27.979842
## iter 80 value 26.838072
## iter 90 value 25.393339
## iter 100 value 24.622412
## final value 24.622412
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 628.828318
## iter 10 value 77.286084
## iter 20 value 48.199439
## iter 30 value 36.251258
## iter 40 value 32.096722
## iter 50 value 30.489283
## iter 60 value 29.759831
## iter 70 value 29.184044
## iter 80 value 28.409858
## iter 90 value 27.708832
## iter 100 value 26.919205
## final value 26.919205
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1110.929023
## iter 10 value 97.833828
## iter 20 value 59.714513
## iter 30 value 37.312691
## iter 40 value 26.020965
## iter 50 value 22.571155
## iter 60 value 20.739695
## iter 70 value 18.693733
## iter 80 value 18.143173
## iter 90 value 15.453138
## iter 100 value 14.508451
## final value 14.508451
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 692.305238
## iter 10 value 74.277171
## iter 20 value 42.665256
## iter 30 value 27.531811
## iter 40 value 18.033506
## iter 50 value 13.377464
## iter 60 value 10.768733
## iter 70 value 9.922330
## iter 80 value 8.935259
## iter 90 value 6.793743
## iter 100 value 5.969667
## final value 5.969667
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 865.840591
## iter 10 value 119.279535
## iter 20 value 76.494190
## iter 30 value 60.296135
## iter 40 value 47.225912
## iter 50 value 32.705415
## iter 60 value 25.094455
## iter 70 value 20.618936
## iter 80 value 17.220227
## iter 90 value 15.619106
## iter 100 value 14.565029
## final value 14.565029
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1501.221181
## iter 10 value 90.243672
## iter 20 value 49.606825
## iter 30 value 32.177726
## iter 40 value 23.841478
## iter 50 value 21.674666
## iter 60 value 20.289294
## iter 70 value 19.225893
## iter 80 value 18.756494
## iter 90 value 18.493218
## iter 100 value 18.383552
## final value 18.383552
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1308.206121
## iter 10 value 125.039225
## iter 20 value 72.678816
## iter 30 value 49.445617
## iter 40 value 34.816827
## iter 50 value 29.200805
## iter 60 value 27.416924
## iter 70 value 26.113259
## iter 80 value 25.510193
## iter 90 value 25.139640
## iter 100 value 24.812779
## final value 24.812779
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1143.929649
## iter 10 value 143.600564
## iter 20 value 70.976472
## iter 30 value 57.261732
## iter 40 value 52.399366
## iter 50 value 48.218394
## iter 60 value 45.377548
## iter 70 value 43.991056
## iter 80 value 42.986607
## iter 90 value 41.752270
## iter 100 value 38.786734
## final value 38.786734
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 896.163096
## iter 10 value 107.071988
## iter 20 value 65.509945
## iter 30 value 47.240462
## iter 40 value 43.119774
## iter 50 value 40.659970
## iter 60 value 39.227308
## iter 70 value 37.903895
## iter 80 value 36.504641
## iter 90 value 33.462955
## iter 100 value 32.734777
## final value 32.734777
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1096.307033
## iter 10 value 145.028072
## iter 20 value 96.442753
## iter 30 value 71.357545
## iter 40 value 64.320857
## iter 50 value 52.558285
## iter 60 value 43.846051
## iter 70 value 38.489145
## iter 80 value 33.753895
## iter 90 value 30.239030
## iter 100 value 29.133842
## final value 29.133842
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 899.420602
## iter 10 value 120.334376
## iter 20 value 85.229808
## iter 30 value 55.051945
## iter 40 value 34.973008
## iter 50 value 27.270935
## iter 60 value 25.262963
## iter 70 value 24.492811
## iter 80 value 22.971446
## iter 90 value 22.016184
## iter 100 value 20.349750
## final value 20.349750
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 862.272745
## iter 10 value 129.200818
## iter 20 value 82.523982
## iter 30 value 65.931438
## iter 40 value 53.093163
## iter 50 value 47.545358
## iter 60 value 36.986450
## iter 70 value 30.417869
## iter 80 value 27.730118
## iter 90 value 25.837171
## iter 100 value 19.724046
## final value 19.724046
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1262.712244
## iter 10 value 170.519084
## iter 20 value 111.090633
## iter 30 value 91.338907
## iter 40 value 77.028066
## iter 50 value 60.481907
## iter 60 value 52.814655
## iter 70 value 49.292725
## iter 80 value 44.590089
## iter 90 value 42.088867
## iter 100 value 39.287090
## final value 39.287090
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 798.439177
## iter 10 value 168.436064
## iter 20 value 120.249858
## iter 30 value 91.240090
## iter 40 value 80.800660
## iter 50 value 72.253885
## iter 60 value 66.382716
## iter 70 value 61.034528
## iter 80 value 55.573381
## iter 90 value 52.465924
## iter 100 value 48.297932
## final value 48.297932
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 940.841811
## iter 10 value 111.766918
## iter 20 value 61.567875
## iter 30 value 34.897023
## iter 40 value 21.084618
## iter 50 value 11.954614
## iter 60 value 9.862225
## iter 70 value 8.712313
## iter 80 value 7.508629
## iter 90 value 6.624663
## iter 100 value 6.099956
## final value 6.099956
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1282.493396
## iter 10 value 101.841879
## iter 20 value 63.923915
## iter 30 value 36.147552
## iter 40 value 21.458900
## iter 50 value 17.867202
## iter 60 value 16.317962
## iter 70 value 15.547577
## iter 80 value 13.444305
## iter 90 value 10.598989
## iter 100 value 7.692040
## final value 7.692040
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1530.206838
## iter 10 value 100.406608
## iter 20 value 49.357032
## iter 30 value 23.736073
## iter 40 value 14.756649
## iter 50 value 11.159552
## iter 60 value 9.639123
## iter 70 value 9.175546
## iter 80 value 7.747127
## iter 90 value 7.165190
## iter 100 value 6.840433
## final value 6.840433
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1812.646316
## iter 10 value 120.361592
## iter 20 value 66.290774
## iter 30 value 49.134838
## iter 40 value 37.693338
## iter 50 value 33.565908
## iter 60 value 26.467636
## iter 70 value 24.839869
## iter 80 value 23.310029
## iter 90 value 21.716260
## iter 100 value 21.058672
## final value 21.058672
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 931.605128
## iter 10 value 114.527683
## iter 20 value 70.762005
## iter 30 value 44.975910
## iter 40 value 29.895399
## iter 50 value 25.925048
## iter 60 value 24.531652
## iter 70 value 23.479045
## iter 80 value 22.724173
## iter 90 value 22.421511
## iter 100 value 22.245268
## final value 22.245268
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1021.265801
## iter 10 value 97.580959
## iter 20 value 57.172843
## iter 30 value 38.039262
## iter 40 value 25.397739
## iter 50 value 17.770171
## iter 60 value 13.111429
## iter 70 value 11.658329
## iter 80 value 10.999806
## iter 90 value 10.744237
## iter 100 value 10.598704
## final value 10.598704
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 849.686325
## iter 10 value 114.383242
## iter 20 value 71.375485
## iter 30 value 52.966841
## iter 40 value 42.444074
## iter 50 value 38.558806
## iter 60 value 36.807082
## iter 70 value 34.954464
## iter 80 value 33.368403
## iter 90 value 32.965106
## iter 100 value 32.790744
## final value 32.790744
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 767.421019
## iter 10 value 136.452127
## iter 20 value 90.684739
## iter 30 value 62.452015
## iter 40 value 44.117585
## iter 50 value 37.847176
## iter 60 value 34.729302
## iter 70 value 32.748945
## iter 80 value 31.786345
## iter 90 value 30.490919
## iter 100 value 29.937210
## final value 29.937210
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1065.425972
## iter 10 value 105.486648
## iter 20 value 63.663382
## iter 30 value 46.256866
## iter 40 value 35.632006
## iter 50 value 27.220122
## iter 60 value 18.268626
## iter 70 value 16.953582
## iter 80 value 15.309988
## iter 90 value 14.077036
## iter 100 value 13.325719
## final value 13.325719
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1201.120818
## iter 10 value 95.236611
## iter 20 value 60.631355
## iter 30 value 38.665907
## iter 40 value 20.786122
## iter 50 value 16.324100
## iter 60 value 15.358073
## iter 70 value 14.879513
## iter 80 value 14.646307
## iter 90 value 14.475665
## iter 100 value 14.212426
## final value 14.212426
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 898.024588
## iter 10 value 159.474578
## iter 20 value 104.700915
## iter 30 value 61.034481
## iter 40 value 40.751900
## iter 50 value 37.259461
## iter 60 value 35.518504
## iter 70 value 31.488453
## iter 80 value 29.584904
## iter 90 value 28.487693
## iter 100 value 28.002595
## final value 28.002595
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 916.602804
## iter 10 value 121.374843
## iter 20 value 58.766626
## iter 30 value 42.628090
## iter 40 value 32.931735
## iter 50 value 29.030767
## iter 60 value 27.663961
## iter 70 value 26.623861
## iter 80 value 24.371593
## iter 90 value 23.190890
## iter 100 value 22.273309
## final value 22.273309
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 941.316291
## iter 10 value 131.753190
## iter 20 value 73.223011
## iter 30 value 55.045012
## iter 40 value 50.012444
## iter 50 value 44.484817
## iter 60 value 36.826407
## iter 70 value 31.381545
## iter 80 value 25.050219
## iter 90 value 23.539228
## iter 100 value 22.959688
## final value 22.959688
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 887.165549
## iter 10 value 111.193859
## iter 20 value 58.903292
## iter 30 value 34.786451
## iter 40 value 26.388456
## iter 50 value 22.977795
## iter 60 value 20.800519
## iter 70 value 19.248089
## iter 80 value 18.471766
## iter 90 value 17.240999
## iter 100 value 16.577690
## final value 16.577690
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 795.726549
## iter 10 value 115.489906
## iter 20 value 81.838399
## iter 30 value 61.529701
## iter 40 value 47.171221
## iter 50 value 36.136315
## iter 60 value 28.702235
## iter 70 value 25.750239
## iter 80 value 22.983363
## iter 90 value 20.366156
## iter 100 value 19.271689
## final value 19.271689
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 752.502721
## iter 10 value 131.553078
## iter 20 value 72.965168
## iter 30 value 50.875351
## iter 40 value 36.816580
## iter 50 value 32.689140
## iter 60 value 29.641665
## iter 70 value 28.571877
## iter 80 value 25.175326
## iter 90 value 22.848996
## iter 100 value 21.915872
## final value 21.915872
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 853.398714
## iter 10 value 158.113890
## iter 20 value 113.476642
## iter 30 value 87.459649
## iter 40 value 69.840485
## iter 50 value 57.378227
## iter 60 value 49.353787
## iter 70 value 46.561554
## iter 80 value 43.342047
## iter 90 value 40.175946
## iter 100 value 38.108514
## final value 38.108514
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 754.907155
## iter 10 value 162.592426
## iter 20 value 86.449380
## iter 30 value 51.346072
## iter 40 value 38.950873
## iter 50 value 35.501842
## iter 60 value 34.445598
## iter 70 value 33.532094
## iter 80 value 32.413430
## iter 90 value 31.226408
## iter 100 value 29.425260
## final value 29.425260
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1119.140838
## iter 10 value 94.816320
## iter 20 value 53.517146
## iter 30 value 35.292057
## iter 40 value 24.538745
## iter 50 value 22.605490
## iter 60 value 21.555546
## iter 70 value 20.812005
## iter 80 value 19.527575
## iter 90 value 17.862671
## iter 100 value 17.197654
## final value 17.197654
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1225.152028
## iter 10 value 114.634250
## iter 20 value 63.135035
## iter 30 value 39.580916
## iter 40 value 21.435471
## iter 50 value 14.227098
## iter 60 value 12.737268
## iter 70 value 12.060089
## iter 80 value 11.512655
## iter 90 value 11.251570
## iter 100 value 11.005232
## final value 11.005232
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1019.739879
## iter 10 value 84.452797
## iter 20 value 49.759049
## iter 30 value 30.069126
## iter 40 value 23.297462
## iter 50 value 21.701061
## iter 60 value 20.622922
## iter 70 value 20.352875
## iter 80 value 20.205248
## iter 90 value 20.048664
## iter 100 value 19.972379
## final value 19.972379
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1194.583709
## iter 10 value 118.319030
## iter 20 value 70.748769
## iter 30 value 52.572900
## iter 40 value 41.214218
## iter 50 value 36.736221
## iter 60 value 34.094023
## iter 70 value 27.517380
## iter 80 value 22.108935
## iter 90 value 19.969107
## iter 100 value 19.421282
## final value 19.421282
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1468.978937
## iter 10 value 141.632813
## iter 20 value 93.312119
## iter 30 value 51.699441
## iter 40 value 31.506666
## iter 50 value 22.990156
## iter 60 value 17.238789
## iter 70 value 16.156863
## iter 80 value 15.637024
## iter 90 value 14.111559
## iter 100 value 9.244880
## final value 9.244880
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 763.070034
## iter 10 value 134.960775
## iter 20 value 98.363975
## iter 30 value 85.822429
## iter 40 value 65.240105
## iter 50 value 49.328685
## iter 60 value 41.768471
## iter 70 value 35.735788
## iter 80 value 29.909148
## iter 90 value 28.024585
## iter 100 value 26.280125
## final value 26.280125
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 721.281387
## iter 10 value 146.770835
## iter 20 value 84.209554
## iter 30 value 52.590843
## iter 40 value 34.384807
## iter 50 value 21.584486
## iter 60 value 16.958125
## iter 70 value 15.669977
## iter 80 value 14.455174
## iter 90 value 12.592604
## iter 100 value 11.795633
## final value 11.795633
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 921.933717
## iter 10 value 120.451668
## iter 20 value 70.589396
## iter 30 value 45.387666
## iter 40 value 32.238940
## iter 50 value 23.787571
## iter 60 value 21.174211
## iter 70 value 19.365472
## iter 80 value 18.732415
## iter 90 value 18.127762
## iter 100 value 17.710571
## final value 17.710571
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 664.250340
## iter 10 value 118.694032
## iter 20 value 79.227307
## iter 30 value 49.141582
## iter 40 value 38.776847
## iter 50 value 35.543564
## iter 60 value 32.755293
## iter 70 value 30.424756
## iter 80 value 29.308696
## iter 90 value 28.754122
## iter 100 value 28.413590
## final value 28.413590
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 781.829146
## iter 10 value 145.870947
## iter 20 value 88.260830
## iter 30 value 53.386900
## iter 40 value 37.056700
## iter 50 value 29.588793
## iter 60 value 23.295574
## iter 70 value 22.280840
## iter 80 value 21.716654
## iter 90 value 21.083423
## iter 100 value 20.567025
## final value 20.567025
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 870.252807
## iter 10 value 150.992695
## iter 20 value 94.280299
## iter 30 value 61.341700
## iter 40 value 52.404610
## iter 50 value 44.450609
## iter 60 value 37.417297
## iter 70 value 33.894724
## iter 80 value 32.824388
## iter 90 value 31.784834
## iter 100 value 31.174431
## final value 31.174431
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 688.246258
## iter 10 value 87.090137
## iter 20 value 46.056525
## iter 30 value 28.894289
## iter 40 value 18.873204
## iter 50 value 14.796399
## iter 60 value 13.269203
## iter 70 value 12.562626
## iter 80 value 12.149038
## iter 90 value 11.507870
## iter 100 value 10.356999
## final value 10.356999
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 683.716594
## iter 10 value 143.857458
## iter 20 value 93.593795
## iter 30 value 58.373753
## iter 40 value 44.236232
## iter 50 value 39.488205
## iter 60 value 36.370327
## iter 70 value 34.698047
## iter 80 value 32.565047
## iter 90 value 31.406025
## iter 100 value 30.155002
## final value 30.155002
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1203.399468
## iter 10 value 99.016898
## iter 20 value 52.956141
## iter 30 value 31.689742
## iter 40 value 24.122921
## iter 50 value 20.900680
## iter 60 value 18.896855
## iter 70 value 17.483121
## iter 80 value 16.800145
## iter 90 value 16.345478
## iter 100 value 15.537424
## final value 15.537424
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1089.022902
## iter 10 value 97.671864
## iter 20 value 63.069301
## iter 30 value 42.346955
## iter 40 value 31.972876
## iter 50 value 27.156536
## iter 60 value 24.340208
## iter 70 value 23.583804
## iter 80 value 22.707498
## iter 90 value 22.118885
## iter 100 value 21.952941
## final value 21.952941
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1305.976172
## iter 10 value 103.555986
## iter 20 value 65.961640
## iter 30 value 44.981279
## iter 40 value 26.282511
## iter 50 value 14.511588
## iter 60 value 11.022855
## iter 70 value 10.133573
## iter 80 value 9.610681
## iter 90 value 9.322837
## iter 100 value 9.029470
## final value 9.029470
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 991.458467
## iter 10 value 130.876763
## iter 20 value 75.434423
## iter 30 value 54.424076
## iter 40 value 42.025516
## iter 50 value 32.942690
## iter 60 value 26.626988
## iter 70 value 25.762304
## iter 80 value 25.130116
## iter 90 value 24.613679
## iter 100 value 24.263291
## final value 24.263291
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 662.284468
## iter 10 value 133.752578
## iter 20 value 73.808282
## iter 30 value 52.994556
## iter 40 value 43.009623
## iter 50 value 39.559617
## iter 60 value 38.285915
## iter 70 value 37.497296
## iter 80 value 36.912195
## iter 90 value 35.746142
## iter 100 value 33.458102
## final value 33.458102
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 724.841079
## iter 10 value 95.818843
## iter 20 value 56.716256
## iter 30 value 42.170911
## iter 40 value 38.161130
## iter 50 value 36.222270
## iter 60 value 34.092737
## iter 70 value 33.198486
## iter 80 value 31.546566
## iter 90 value 29.106203
## iter 100 value 28.410253
## final value 28.410253
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 824.557607
## iter 10 value 98.009565
## iter 20 value 70.520849
## iter 30 value 52.132048
## iter 40 value 42.921376
## iter 50 value 34.506443
## iter 60 value 29.848757
## iter 70 value 27.183218
## iter 80 value 25.089127
## iter 90 value 22.740110
## iter 100 value 20.391329
## final value 20.391329
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 2022.606361
## iter 10 value 109.716820
## iter 20 value 66.396879
## iter 30 value 36.300732
## iter 40 value 21.539916
## iter 50 value 17.907803
## iter 60 value 16.612498
## iter 70 value 15.817623
## iter 80 value 15.049617
## iter 90 value 14.444036
## iter 100 value 13.935672
## final value 13.935672
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1066.472388
## iter 10 value 112.653372
## iter 20 value 77.050470
## iter 30 value 53.970940
## iter 40 value 33.125792
## iter 50 value 20.117520
## iter 60 value 16.593242
## iter 70 value 15.226365
## iter 80 value 13.910487
## iter 90 value 13.476391
## iter 100 value 13.196194
## final value 13.196194
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1157.255627
## iter 10 value 110.125060
## iter 20 value 81.328043
## iter 30 value 57.490138
## iter 40 value 44.217096
## iter 50 value 38.091300
## iter 60 value 36.137902
## iter 70 value 34.936360
## iter 80 value 32.991075
## iter 90 value 31.286909
## iter 100 value 30.583300
## final value 30.583300
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 814.228646
## iter 10 value 116.314492
## iter 20 value 73.891566
## iter 30 value 46.041182
## iter 40 value 37.078844
## iter 50 value 30.126540
## iter 60 value 28.698207
## iter 70 value 25.015459
## iter 80 value 22.650290
## iter 90 value 21.795239
## iter 100 value 21.031547
## final value 21.031547
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 641.755395
## iter 10 value 107.783069
## iter 20 value 62.694012
## iter 30 value 27.179509
## iter 40 value 15.284554
## iter 50 value 12.080894
## iter 60 value 11.182048
## iter 70 value 10.697577
## iter 80 value 10.290341
## iter 90 value 9.893395
## iter 100 value 9.728276
## final value 9.728276
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 810.879395
## iter 10 value 105.104270
## iter 20 value 53.256632
## iter 30 value 36.293382
## iter 40 value 31.328315
## iter 50 value 27.500406
## iter 60 value 24.637682
## iter 70 value 21.040870
## iter 80 value 20.070257
## iter 90 value 19.338249
## iter 100 value 18.284229
## final value 18.284229
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 912.845701
## iter 10 value 111.649065
## iter 20 value 72.363558
## iter 30 value 49.007830
## iter 40 value 35.494446
## iter 50 value 29.651193
## iter 60 value 23.597540
## iter 70 value 20.890265
## iter 80 value 17.184138
## iter 90 value 16.249433
## iter 100 value 15.522910
## final value 15.522910
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 859.855123
## iter 10 value 133.170522
## iter 20 value 101.843301
## iter 30 value 73.197758
## iter 40 value 57.192593
## iter 50 value 45.437517
## iter 60 value 39.430364
## iter 70 value 37.672237
## iter 80 value 36.444842
## iter 90 value 35.556495
## iter 100 value 35.087240
## final value 35.087240
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 841.489722
## iter 10 value 148.229915
## iter 20 value 102.596595
## iter 30 value 78.118038
## iter 40 value 64.021496
## iter 50 value 57.557266
## iter 60 value 54.582263
## iter 70 value 49.728089
## iter 80 value 47.787107
## iter 90 value 46.408642
## iter 100 value 44.800850
## final value 44.800850
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1001.048331
## iter 10 value 92.388537
## iter 20 value 62.235034
## iter 30 value 35.053051
## iter 40 value 28.609670
## iter 50 value 27.115679
## iter 60 value 25.917621
## iter 70 value 24.785498
## iter 80 value 23.406569
## iter 90 value 22.923433
## iter 100 value 22.498866
## final value 22.498866
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1071.554449
## iter 10 value 117.202988
## iter 20 value 80.209923
## iter 30 value 47.699256
## iter 40 value 18.598482
## iter 50 value 14.208827
## iter 60 value 12.450779
## iter 70 value 11.023837
## iter 80 value 10.366942
## iter 90 value 10.030868
## iter 100 value 9.612095
## final value 9.612095
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1056.252578
## iter 10 value 119.199930
## iter 20 value 79.958178
## iter 30 value 64.582435
## iter 40 value 53.550507
## iter 50 value 44.376390
## iter 60 value 40.905864
## iter 70 value 39.593915
## iter 80 value 38.170205
## iter 90 value 36.279303
## iter 100 value 34.542872
## final value 34.542872
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1044.446674
## iter 10 value 139.607614
## iter 20 value 65.721177
## iter 30 value 49.861286
## iter 40 value 41.787978
## iter 50 value 39.938306
## iter 60 value 39.035404
## iter 70 value 37.919465
## iter 80 value 36.930993
## iter 90 value 36.352011
## iter 100 value 35.516183
## final value 35.516183
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1002.062278
## iter 10 value 118.252472
## iter 20 value 81.173395
## iter 30 value 55.879545
## iter 40 value 27.815496
## iter 50 value 17.695384
## iter 60 value 14.155204
## iter 70 value 12.111294
## iter 80 value 10.685498
## iter 90 value 9.231034
## iter 100 value 8.610992
## final value 8.610992
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 972.904208
## iter 10 value 183.682164
## iter 20 value 120.550851
## iter 30 value 88.559171
## iter 40 value 67.369605
## iter 50 value 57.442585
## iter 60 value 54.765343
## iter 70 value 52.831144
## iter 80 value 51.192601
## iter 90 value 50.272333
## iter 100 value 49.524311
## final value 49.524311
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 904.286686
## iter 10 value 127.441318
## iter 20 value 69.044594
## iter 30 value 36.282162
## iter 40 value 18.055610
## iter 50 value 12.440011
## iter 60 value 10.697337
## iter 70 value 8.586964
## iter 80 value 6.710649
## iter 90 value 5.956402
## iter 100 value 5.425592
## final value 5.425592
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1202.692920
## iter 10 value 108.570878
## iter 20 value 65.189743
## iter 30 value 40.868007
## iter 40 value 33.201234
## iter 50 value 31.141747
## iter 60 value 30.023620
## iter 70 value 29.110384
## iter 80 value 28.421063
## iter 90 value 26.749617
## iter 100 value 25.778486
## final value 25.778486
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 960.782608
## iter 10 value 173.156586
## iter 20 value 79.579263
## iter 30 value 50.199624
## iter 40 value 31.858067
## iter 50 value 18.585926
## iter 60 value 15.885859
## iter 70 value 14.665459
## iter 80 value 14.062664
## iter 90 value 13.805184
## iter 100 value 13.352890
## final value 13.352890
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 684.577174
## iter 10 value 122.401211
## iter 20 value 77.442571
## iter 30 value 55.147165
## iter 40 value 43.454927
## iter 50 value 37.228963
## iter 60 value 32.513108
## iter 70 value 30.683014
## iter 80 value 29.406135
## iter 90 value 27.985861
## iter 100 value 26.850702
## final value 26.850702
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 861.468395
## iter 10 value 90.985449
## iter 20 value 50.923780
## iter 30 value 33.289523
## iter 40 value 25.907700
## iter 50 value 21.709325
## iter 60 value 19.211409
## iter 70 value 17.254124
## iter 80 value 16.035398
## iter 90 value 15.306749
## iter 100 value 14.850454
## final value 14.850454
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1908.965842
## iter 10 value 179.210022
## iter 20 value 125.230340
## iter 30 value 94.149991
## iter 40 value 78.458944
## iter 50 value 62.464990
## iter 60 value 57.047817
## iter 70 value 52.751737
## iter 80 value 45.656772
## iter 90 value 41.470419
## iter 100 value 39.759083
## final value 39.759083
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 606.271021
## iter 10 value 85.483079
## iter 20 value 59.152037
## iter 30 value 36.641010
## iter 40 value 25.652377
## iter 50 value 17.109428
## iter 60 value 12.217731
## iter 70 value 10.881898
## iter 80 value 9.415202
## iter 90 value 8.158772
## iter 100 value 5.470687
## final value 5.470687
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1557.438959
## iter 10 value 101.129223
## iter 20 value 62.489791
## iter 30 value 42.380419
## iter 40 value 31.329082
## iter 50 value 22.341291
## iter 60 value 20.265774
## iter 70 value 16.606899
## iter 80 value 13.602855
## iter 90 value 12.541088
## iter 100 value 11.777379
## final value 11.777379
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1635.890842
## iter 10 value 111.730895
## iter 20 value 68.902895
## iter 30 value 41.074702
## iter 40 value 22.790565
## iter 50 value 14.772947
## iter 60 value 12.566847
## iter 70 value 11.705929
## iter 80 value 11.088096
## iter 90 value 9.555803
## iter 100 value 8.036325
## final value 8.036325
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 908.013825
## iter 10 value 115.607695
## iter 20 value 70.442736
## iter 30 value 49.319938
## iter 40 value 33.965930
## iter 50 value 25.679819
## iter 60 value 21.755410
## iter 70 value 19.924497
## iter 80 value 18.544648
## iter 90 value 16.136739
## iter 100 value 13.052856
## final value 13.052856
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 706.173114
## iter 10 value 99.590184
## iter 20 value 61.109694
## iter 30 value 43.476816
## iter 40 value 34.174723
## iter 50 value 31.521544
## iter 60 value 28.387292
## iter 70 value 27.240673
## iter 80 value 26.214395
## iter 90 value 24.836844
## iter 100 value 24.252639
## final value 24.252639
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1030.423615
## iter 10 value 136.052295
## iter 20 value 85.086523
## iter 30 value 59.996270
## iter 40 value 45.629754
## iter 50 value 35.724522
## iter 60 value 31.717144
## iter 70 value 28.056905
## iter 80 value 25.657131
## iter 90 value 24.671723
## iter 100 value 23.781769
## final value 23.781769
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 896.891671
## iter 10 value 85.931232
## iter 20 value 50.815840
## iter 30 value 25.838344
## iter 40 value 12.342132
## iter 50 value 6.478602
## iter 60 value 5.527395
## iter 70 value 5.048454
## iter 80 value 4.794981
## iter 90 value 4.582395
## iter 100 value 3.719606
## final value 3.719606
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 920.882473
## iter 10 value 128.421293
## iter 20 value 56.956369
## iter 30 value 28.609821
## iter 40 value 17.572741
## iter 50 value 13.715184
## iter 60 value 12.006518
## iter 70 value 10.651044
## iter 80 value 9.725660
## iter 90 value 8.798785
## iter 100 value 7.725340
## final value 7.725340
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1319.470784
## iter 10 value 119.913126
## iter 20 value 73.652904
## iter 30 value 48.045447
## iter 40 value 33.437800
## iter 50 value 25.681683
## iter 60 value 23.118943
## iter 70 value 21.447074
## iter 80 value 19.860353
## iter 90 value 18.231758
## iter 100 value 16.573125
## final value 16.573125
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1466.682534
## iter 10 value 91.496479
## iter 20 value 60.020121
## iter 30 value 47.096241
## iter 40 value 37.215877
## iter 50 value 34.080944
## iter 60 value 30.984305
## iter 70 value 30.033093
## iter 80 value 29.348696
## iter 90 value 28.800336
## iter 100 value 25.972569
## final value 25.972569
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 805.417203
## iter 10 value 97.321946
## iter 20 value 54.621620
## iter 30 value 32.305792
## iter 40 value 24.619148
## iter 50 value 16.394384
## iter 60 value 11.729778
## iter 70 value 10.366553
## iter 80 value 9.336313
## iter 90 value 8.627564
## iter 100 value 8.002869
## final value 8.002869
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 851.475954
## iter 10 value 151.290249
## iter 20 value 88.362331
## iter 30 value 59.792748
## iter 40 value 34.282469
## iter 50 value 20.169370
## iter 60 value 16.682524
## iter 70 value 15.619317
## iter 80 value 14.877721
## iter 90 value 14.467402
## iter 100 value 14.189459
## final value 14.189459
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 615.790475
## iter 10 value 97.898882
## iter 20 value 54.325633
## iter 30 value 32.305511
## iter 40 value 17.974487
## iter 50 value 13.039395
## iter 60 value 11.123486
## iter 70 value 10.417952
## iter 80 value 9.858997
## iter 90 value 9.298859
## iter 100 value 8.946353
## final value 8.946353
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1509.394948
## iter 10 value 123.627699
## iter 20 value 70.044500
## iter 30 value 42.006429
## iter 40 value 32.091753
## iter 50 value 29.288136
## iter 60 value 28.003162
## iter 70 value 26.724699
## iter 80 value 24.948244
## iter 90 value 24.381515
## iter 100 value 23.690722
## final value 23.690722
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 679.505577
## iter 10 value 131.806822
## iter 20 value 84.849270
## iter 30 value 49.621694
## iter 40 value 32.640102
## iter 50 value 24.387275
## iter 60 value 21.574269
## iter 70 value 20.403502
## iter 80 value 19.766342
## iter 90 value 17.602968
## iter 100 value 16.395509
## final value 16.395509
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 709.660625
## iter 10 value 116.951360
## iter 20 value 65.454729
## iter 30 value 42.542821
## iter 40 value 28.488046
## iter 50 value 23.192569
## iter 60 value 21.702223
## iter 70 value 20.892383
## iter 80 value 18.063994
## iter 90 value 15.946151
## iter 100 value 14.817083
## final value 14.817083
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 750.557953
## iter 10 value 89.986268
## iter 20 value 51.419667
## iter 30 value 30.639056
## iter 40 value 22.924264
## iter 50 value 20.535172
## iter 60 value 19.366810
## iter 70 value 17.505925
## iter 80 value 15.055278
## iter 90 value 12.886787
## iter 100 value 11.447026
## final value 11.447026
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 905.453702
## iter 10 value 161.316516
## iter 20 value 108.994396
## iter 30 value 85.572269
## iter 40 value 52.471111
## iter 50 value 40.234279
## iter 60 value 35.430475
## iter 70 value 32.579818
## iter 80 value 30.384275
## iter 90 value 29.205320
## iter 100 value 28.398831
## final value 28.398831
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 945.112410
## iter 10 value 109.017126
## iter 20 value 63.820654
## iter 30 value 47.828885
## iter 40 value 43.881112
## iter 50 value 41.265982
## iter 60 value 39.107275
## iter 70 value 38.013326
## iter 80 value 37.280826
## iter 90 value 36.084325
## iter 100 value 33.014897
## final value 33.014897
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 701.029007
## iter 10 value 104.584539
## iter 20 value 75.101855
## iter 30 value 53.608681
## iter 40 value 41.581629
## iter 50 value 36.249053
## iter 60 value 30.914690
## iter 70 value 29.259715
## iter 80 value 27.999005
## iter 90 value 26.897532
## iter 100 value 25.670171
## final value 25.670171
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1108.203338
## iter 10 value 145.742393
## iter 20 value 93.212812
## iter 30 value 78.509416
## iter 40 value 61.840011
## iter 50 value 55.591504
## iter 60 value 51.346508
## iter 70 value 49.569344
## iter 80 value 45.609536
## iter 90 value 39.250499
## iter 100 value 36.977004
## final value 36.977004
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1322.366552
## iter 10 value 128.945286
## iter 20 value 70.069034
## iter 30 value 46.004516
## iter 40 value 32.363670
## iter 50 value 26.419479
## iter 60 value 25.284935
## iter 70 value 24.876055
## iter 80 value 24.626479
## iter 90 value 24.477317
## iter 100 value 24.341113
## final value 24.341113
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1086.328996
## iter 10 value 133.772090
## iter 20 value 78.351639
## iter 30 value 47.700994
## iter 40 value 28.385677
## iter 50 value 22.780396
## iter 60 value 21.178813
## iter 70 value 20.091250
## iter 80 value 19.623036
## iter 90 value 19.355051
## iter 100 value 19.220923
## final value 19.220923
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1172.345416
## iter 10 value 145.398781
## iter 20 value 93.163851
## iter 30 value 64.685847
## iter 40 value 52.950057
## iter 50 value 42.873577
## iter 60 value 39.219224
## iter 70 value 37.218829
## iter 80 value 36.040670
## iter 90 value 35.022363
## iter 100 value 31.419417
## final value 31.419417
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 794.023698
## iter 10 value 119.873837
## iter 20 value 62.700328
## iter 30 value 42.490730
## iter 40 value 34.671472
## iter 50 value 30.172488
## iter 60 value 24.683898
## iter 70 value 22.840456
## iter 80 value 21.769008
## iter 90 value 20.766752
## iter 100 value 19.394473
## final value 19.394473
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 779.561700
## iter 10 value 176.853468
## iter 20 value 127.875236
## iter 30 value 93.783145
## iter 40 value 66.787845
## iter 50 value 56.696731
## iter 60 value 53.059596
## iter 70 value 49.529310
## iter 80 value 44.596909
## iter 90 value 42.138641
## iter 100 value 39.919653
## final value 39.919653
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 974.294325
## iter 10 value 107.711808
## iter 20 value 65.744264
## iter 30 value 47.731534
## iter 40 value 34.246556
## iter 50 value 27.208716
## iter 60 value 21.704943
## iter 70 value 20.562362
## iter 80 value 19.962625
## iter 90 value 19.502259
## iter 100 value 19.070804
## final value 19.070804
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 738.728066
## iter 10 value 98.843418
## iter 20 value 53.793983
## iter 30 value 33.372032
## iter 40 value 21.742704
## iter 50 value 18.206404
## iter 60 value 17.136360
## iter 70 value 16.611056
## iter 80 value 15.968814
## iter 90 value 15.618935
## iter 100 value 14.838105
## final value 14.838105
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 860.106673
## iter 10 value 126.699223
## iter 20 value 87.753689
## iter 30 value 67.230127
## iter 40 value 45.832300
## iter 50 value 40.558708
## iter 60 value 37.876605
## iter 70 value 36.770463
## iter 80 value 35.906088
## iter 90 value 34.704445
## iter 100 value 34.004996
## final value 34.004996
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 879.128930
## iter 10 value 166.148863
## iter 20 value 111.616956
## iter 30 value 71.317646
## iter 40 value 52.555036
## iter 50 value 47.359306
## iter 60 value 44.370761
## iter 70 value 42.190259
## iter 80 value 41.417687
## iter 90 value 41.118276
## iter 100 value 40.881899
## final value 40.881899
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 814.438695
## iter 10 value 115.243331
## iter 20 value 81.804745
## iter 30 value 59.343955
## iter 40 value 45.580311
## iter 50 value 41.440367
## iter 60 value 38.386861
## iter 70 value 34.980865
## iter 80 value 31.078791
## iter 90 value 29.093738
## iter 100 value 28.166603
## final value 28.166603
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 688.821409
## iter 10 value 85.223452
## iter 20 value 51.125535
## iter 30 value 23.433876
## iter 40 value 11.503869
## iter 50 value 9.312024
## iter 60 value 6.997757
## iter 70 value 6.236421
## iter 80 value 4.358210
## iter 90 value 3.831434
## iter 100 value 3.432779
## final value 3.432779
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 952.937947
## iter 10 value 93.487024
## iter 20 value 60.468822
## iter 30 value 51.989882
## iter 40 value 36.773004
## iter 50 value 24.114314
## iter 60 value 17.632055
## iter 70 value 16.197782
## iter 80 value 15.543957
## iter 90 value 15.130779
## iter 100 value 14.774496
## final value 14.774496
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 676.755007
## iter 10 value 111.294937
## iter 20 value 69.226032
## iter 30 value 47.443532
## iter 40 value 31.062552
## iter 50 value 27.927549
## iter 60 value 25.757141
## iter 70 value 24.195460
## iter 80 value 23.278334
## iter 90 value 22.301735
## iter 100 value 21.076605
## final value 21.076605
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 921.136138
## iter 10 value 81.290153
## iter 20 value 45.778470
## iter 30 value 27.834997
## iter 40 value 16.169381
## iter 50 value 11.235494
## iter 60 value 9.698085
## iter 70 value 8.684426
## iter 80 value 7.744572
## iter 90 value 6.681185
## iter 100 value 4.450008
## final value 4.450008
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1246.345691
## iter 10 value 134.888969
## iter 20 value 76.980518
## iter 30 value 55.352672
## iter 40 value 38.378309
## iter 50 value 27.275399
## iter 60 value 24.696362
## iter 70 value 23.299497
## iter 80 value 22.416923
## iter 90 value 21.926621
## iter 100 value 19.917526
## final value 19.917526
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 843.899607
## iter 10 value 137.126248
## iter 20 value 90.254206
## iter 30 value 64.229544
## iter 40 value 54.261105
## iter 50 value 52.019945
## iter 60 value 50.475873
## iter 70 value 48.981034
## iter 80 value 47.535989
## iter 90 value 45.748808
## iter 100 value 44.606377
## final value 44.606377
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 598.159958
## iter 10 value 136.521191
## iter 20 value 70.742628
## iter 30 value 50.749757
## iter 40 value 43.176478
## iter 50 value 39.348895
## iter 60 value 36.396420
## iter 70 value 31.051244
## iter 80 value 28.925212
## iter 90 value 27.298706
## iter 100 value 26.344517
## final value 26.344517
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 898.954364
## iter 10 value 115.707143
## iter 20 value 76.770116
## iter 30 value 51.930028
## iter 40 value 40.379849
## iter 50 value 34.510876
## iter 60 value 32.327835
## iter 70 value 30.677074
## iter 80 value 26.733247
## iter 90 value 25.427399
## iter 100 value 24.828597
## final value 24.828597
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1502.592041
## iter 10 value 118.920083
## iter 20 value 77.207838
## iter 30 value 53.209185
## iter 40 value 30.996259
## iter 50 value 25.010345
## iter 60 value 22.295075
## iter 70 value 19.862279
## iter 80 value 17.115841
## iter 90 value 15.909994
## iter 100 value 13.278210
## final value 13.278210
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 755.013483
## iter 10 value 106.563338
## iter 20 value 72.415033
## iter 30 value 53.631323
## iter 40 value 35.409487
## iter 50 value 24.087262
## iter 60 value 21.658489
## iter 70 value 20.202606
## iter 80 value 19.468161
## iter 90 value 18.765293
## iter 100 value 18.566434
## final value 18.566434
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1163.452596
## iter 10 value 162.203173
## iter 20 value 114.996005
## iter 30 value 74.822474
## iter 40 value 57.482955
## iter 50 value 49.531921
## iter 60 value 45.598008
## iter 70 value 43.531337
## iter 80 value 39.784488
## iter 90 value 36.699233
## iter 100 value 35.333797
## final value 35.333797
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 895.021866
## iter 10 value 79.161582
## iter 20 value 50.166438
## iter 30 value 30.760110
## iter 40 value 26.161652
## iter 50 value 18.483924
## iter 60 value 17.147031
## iter 70 value 16.088299
## iter 80 value 15.597702
## iter 90 value 14.822788
## iter 100 value 13.881750
## final value 13.881750
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 944.007098
## iter 10 value 96.903825
## iter 20 value 57.153861
## iter 30 value 42.472189
## iter 40 value 31.775844
## iter 50 value 23.963262
## iter 60 value 20.207703
## iter 70 value 18.882541
## iter 80 value 18.288600
## iter 90 value 18.061890
## iter 100 value 17.801573
## final value 17.801573
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1093.490284
## iter 10 value 114.417283
## iter 20 value 68.173747
## iter 30 value 45.703719
## iter 40 value 38.486190
## iter 50 value 35.726268
## iter 60 value 34.437086
## iter 70 value 33.189011
## iter 80 value 32.384899
## iter 90 value 31.550402
## iter 100 value 28.151651
## final value 28.151651
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 719.507944
## iter 10 value 114.315383
## iter 20 value 63.843814
## iter 30 value 54.150679
## iter 40 value 48.117408
## iter 50 value 41.845353
## iter 60 value 39.994754
## iter 70 value 39.193198
## iter 80 value 38.831996
## iter 90 value 38.467315
## iter 100 value 38.110030
## final value 38.110030
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 630.095309
## iter 10 value 93.586476
## iter 20 value 52.027953
## iter 30 value 40.338482
## iter 40 value 36.307093
## iter 50 value 31.756862
## iter 60 value 28.726421
## iter 70 value 26.554785
## iter 80 value 24.841140
## iter 90 value 23.109059
## iter 100 value 22.328879
## final value 22.328879
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 928.198957
## iter 10 value 130.805188
## iter 20 value 84.589597
## iter 30 value 63.968292
## iter 40 value 54.021770
## iter 50 value 38.212580
## iter 60 value 34.721885
## iter 70 value 32.436929
## iter 80 value 31.003244
## iter 90 value 30.424261
## iter 100 value 29.884149
## final value 29.884149
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1304.349600
## iter 10 value 112.959939
## iter 20 value 70.077055
## iter 30 value 46.941840
## iter 40 value 34.005117
## iter 50 value 31.048815
## iter 60 value 29.482349
## iter 70 value 28.856504
## iter 80 value 27.999563
## iter 90 value 26.265306
## iter 100 value 25.574811
## final value 25.574811
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1073.716285
## iter 10 value 133.239664
## iter 20 value 82.062207
## iter 30 value 62.279932
## iter 40 value 41.859291
## iter 50 value 34.713375
## iter 60 value 31.547201
## iter 70 value 30.078904
## iter 80 value 29.395339
## iter 90 value 28.917715
## iter 100 value 28.591406
## final value 28.591406
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1639.358558
## iter 10 value 132.172499
## iter 20 value 83.643555
## iter 30 value 60.266653
## iter 40 value 45.953232
## iter 50 value 35.926615
## iter 60 value 29.208345
## iter 70 value 27.794532
## iter 80 value 26.768812
## iter 90 value 25.988168
## iter 100 value 25.546729
## final value 25.546729
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 998.625720
## iter 10 value 101.912434
## iter 20 value 72.161524
## iter 30 value 55.140353
## iter 40 value 45.783558
## iter 50 value 42.742015
## iter 60 value 38.824448
## iter 70 value 36.377273
## iter 80 value 34.695615
## iter 90 value 33.408278
## iter 100 value 31.235286
## final value 31.235286
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 961.502334
## iter 10 value 114.316850
## iter 20 value 74.136171
## iter 30 value 47.434494
## iter 40 value 38.735023
## iter 50 value 36.589796
## iter 60 value 35.453805
## iter 70 value 35.138065
## iter 80 value 34.547977
## iter 90 value 33.882320
## iter 100 value 33.352304
## final value 33.352304
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1491.594765
## iter 10 value 128.869241
## iter 20 value 95.890398
## iter 30 value 61.855665
## iter 40 value 49.354679
## iter 50 value 43.929104
## iter 60 value 42.344618
## iter 70 value 41.339485
## iter 80 value 40.648477
## iter 90 value 39.618470
## iter 100 value 39.270601
## final value 39.270601
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 911.537762
## iter 10 value 137.583927
## iter 20 value 70.688447
## iter 30 value 49.247039
## iter 40 value 43.362156
## iter 50 value 41.658621
## iter 60 value 40.832981
## iter 70 value 39.301138
## iter 80 value 38.539910
## iter 90 value 37.588164
## iter 100 value 37.039652
## final value 37.039652
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 767.983643
## iter 10 value 120.995155
## iter 20 value 67.664350
## iter 30 value 38.583566
## iter 40 value 30.004332
## iter 50 value 26.446612
## iter 60 value 23.536539
## iter 70 value 18.905759
## iter 80 value 16.030647
## iter 90 value 14.439559
## iter 100 value 13.510786
## final value 13.510786
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1350.724326
## iter 10 value 93.776190
## iter 20 value 60.505602
## iter 30 value 36.407394
## iter 40 value 21.929105
## iter 50 value 17.453708
## iter 60 value 15.595708
## iter 70 value 13.823681
## iter 80 value 11.427394
## iter 90 value 10.189145
## iter 100 value 9.195433
## final value 9.195433
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1189.580459
## iter 10 value 107.467515
## iter 20 value 63.430448
## iter 30 value 44.440917
## iter 40 value 29.907509
## iter 50 value 20.491041
## iter 60 value 18.259467
## iter 70 value 16.902734
## iter 80 value 16.363612
## iter 90 value 14.682151
## iter 100 value 13.337126
## final value 13.337126
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 826.770650
## iter 10 value 112.991863
## iter 20 value 78.459774
## iter 30 value 58.011463
## iter 40 value 43.801913
## iter 50 value 24.465343
## iter 60 value 18.246966
## iter 70 value 16.524251
## iter 80 value 15.519386
## iter 90 value 14.854299
## iter 100 value 14.433753
## final value 14.433753
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 846.180272
## iter 10 value 136.131544
## iter 20 value 94.738520
## iter 30 value 77.841376
## iter 40 value 53.800954
## iter 50 value 36.451298
## iter 60 value 32.535766
## iter 70 value 29.626652
## iter 80 value 28.122216
## iter 90 value 27.294724
## iter 100 value 26.464702
## final value 26.464702
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1090.864780
## iter 10 value 134.169370
## iter 20 value 94.611132
## iter 30 value 64.179415
## iter 40 value 45.457700
## iter 50 value 38.344423
## iter 60 value 35.378538
## iter 70 value 34.042474
## iter 80 value 32.978032
## iter 90 value 29.025328
## iter 100 value 26.706074
## final value 26.706074
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 769.002019
## iter 10 value 154.198478
## iter 20 value 88.024574
## iter 30 value 59.018324
## iter 40 value 49.594047
## iter 50 value 43.860999
## iter 60 value 42.489911
## iter 70 value 41.373164
## iter 80 value 39.858271
## iter 90 value 39.430721
## iter 100 value 38.670406
## final value 38.670406
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1027.695034
## iter 10 value 145.166115
## iter 20 value 77.568975
## iter 30 value 45.757080
## iter 40 value 27.739218
## iter 50 value 17.094858
## iter 60 value 14.754375
## iter 70 value 13.395958
## iter 80 value 12.529612
## iter 90 value 11.995174
## iter 100 value 11.569460
## final value 11.569460
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1169.031629
## iter 10 value 141.691605
## iter 20 value 93.568568
## iter 30 value 71.074642
## iter 40 value 58.377882
## iter 50 value 49.069332
## iter 60 value 40.093168
## iter 70 value 37.248583
## iter 80 value 35.004491
## iter 90 value 34.094539
## iter 100 value 33.692777
## final value 33.692777
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1170.602413
## iter 10 value 172.471459
## iter 20 value 107.941825
## iter 30 value 78.083110
## iter 40 value 64.816013
## iter 50 value 58.359677
## iter 60 value 55.289929
## iter 70 value 51.743900
## iter 80 value 49.886500
## iter 90 value 46.616962
## iter 100 value 45.456577
## final value 45.456577
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 965.989908
## iter 10 value 172.013367
## iter 20 value 107.353519
## iter 30 value 89.363993
## iter 40 value 76.758807
## iter 50 value 64.982900
## iter 60 value 58.354289
## iter 70 value 56.338859
## iter 80 value 55.080339
## iter 90 value 53.860817
## iter 100 value 52.057795
## final value 52.057795
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 929.499272
## iter 10 value 99.877682
## iter 20 value 77.619888
## iter 30 value 57.845545
## iter 40 value 37.677450
## iter 50 value 29.376630
## iter 60 value 26.326659
## iter 70 value 23.266297
## iter 80 value 20.438270
## iter 90 value 16.538779
## iter 100 value 15.042455
## final value 15.042455
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1276.170019
## iter 10 value 110.681194
## iter 20 value 83.904185
## iter 30 value 68.303510
## iter 40 value 57.393851
## iter 50 value 46.270687
## iter 60 value 41.564706
## iter 70 value 39.846059
## iter 80 value 38.576630
## iter 90 value 37.907422
## iter 100 value 36.753257
## final value 36.753257
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1367.669452
## iter 10 value 108.208756
## iter 20 value 71.889059
## iter 30 value 53.463527
## iter 40 value 42.478088
## iter 50 value 35.085574
## iter 60 value 31.976857
## iter 70 value 30.176894
## iter 80 value 26.367228
## iter 90 value 24.458461
## iter 100 value 23.237253
## final value 23.237253
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1010.444679
## iter 10 value 101.196339
## iter 20 value 67.026670
## iter 30 value 53.496210
## iter 40 value 47.815398
## iter 50 value 41.284363
## iter 60 value 39.049634
## iter 70 value 36.692077
## iter 80 value 35.185828
## iter 90 value 32.100893
## iter 100 value 31.107841
## final value 31.107841
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 728.307278
## iter 10 value 118.851750
## iter 20 value 71.706504
## iter 30 value 41.517378
## iter 40 value 31.121041
## iter 50 value 28.198797
## iter 60 value 27.106542
## iter 70 value 25.722929
## iter 80 value 23.137445
## iter 90 value 22.391222
## iter 100 value 21.554285
## final value 21.554285
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 840.841986
## iter 10 value 157.457664
## iter 20 value 77.688861
## iter 30 value 59.221517
## iter 40 value 51.959944
## iter 50 value 47.721366
## iter 60 value 46.518572
## iter 70 value 45.709918
## iter 80 value 45.343624
## iter 90 value 45.196954
## iter 100 value 45.040485
## final value 45.040485
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 771.960600
## iter 10 value 135.695836
## iter 20 value 82.732736
## iter 30 value 69.136384
## iter 40 value 62.474555
## iter 50 value 57.564031
## iter 60 value 55.368469
## iter 70 value 53.120715
## iter 80 value 52.251200
## iter 90 value 50.379937
## iter 100 value 48.304166
## final value 48.304166
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1534.221770
## iter 10 value 113.965221
## iter 20 value 76.826037
## iter 30 value 51.891328
## iter 40 value 37.470455
## iter 50 value 31.657367
## iter 60 value 28.846725
## iter 70 value 26.214874
## iter 80 value 25.115288
## iter 90 value 24.611550
## iter 100 value 24.350928
## final value 24.350928
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 742.286701
## iter 10 value 131.191025
## iter 20 value 79.574328
## iter 30 value 58.698497
## iter 40 value 41.090799
## iter 50 value 34.921539
## iter 60 value 30.595698
## iter 70 value 28.433807
## iter 80 value 26.117714
## iter 90 value 24.916474
## iter 100 value 23.943184
## final value 23.943184
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1836.542380
## iter 10 value 109.170194
## iter 20 value 57.045491
## iter 30 value 36.274499
## iter 40 value 29.565819
## iter 50 value 28.231776
## iter 60 value 22.678915
## iter 70 value 20.727495
## iter 80 value 19.142182
## iter 90 value 18.595766
## iter 100 value 18.222666
## final value 18.222666
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 933.760678
## iter 10 value 125.972571
## iter 20 value 69.290217
## iter 30 value 55.502799
## iter 40 value 43.469014
## iter 50 value 40.036376
## iter 60 value 38.185627
## iter 70 value 37.332460
## iter 80 value 35.948239
## iter 90 value 34.428907
## iter 100 value 32.830952
## final value 32.830952
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 945.883524
## iter 10 value 149.278132
## iter 20 value 111.738545
## iter 30 value 78.451778
## iter 40 value 63.196260
## iter 50 value 48.670886
## iter 60 value 44.867289
## iter 70 value 42.251689
## iter 80 value 40.829288
## iter 90 value 39.477729
## iter 100 value 38.915207
## final value 38.915207
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 859.334608
## iter 10 value 114.774434
## iter 20 value 79.963619
## iter 30 value 67.820126
## iter 40 value 60.542345
## iter 50 value 56.923529
## iter 60 value 54.547447
## iter 70 value 51.080436
## iter 80 value 47.358712
## iter 90 value 42.613835
## iter 100 value 39.830214
## final value 39.830214
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 975.341757
## iter 10 value 128.321132
## iter 20 value 86.572217
## iter 30 value 49.091642
## iter 40 value 38.415456
## iter 50 value 33.299747
## iter 60 value 31.594853
## iter 70 value 30.687922
## iter 80 value 28.888352
## iter 90 value 27.530958
## iter 100 value 26.966099
## final value 26.966099
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 955.545518
## iter 10 value 117.635201
## iter 20 value 85.936850
## iter 30 value 60.366845
## iter 40 value 42.481002
## iter 50 value 29.496924
## iter 60 value 25.934930
## iter 70 value 24.336603
## iter 80 value 23.608251
## iter 90 value 23.177811
## iter 100 value 22.617072
## final value 22.617072
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 859.873254
## iter 10 value 104.306802
## iter 20 value 55.167981
## iter 30 value 43.482665
## iter 40 value 36.040718
## iter 50 value 32.422034
## iter 60 value 31.237501
## iter 70 value 30.652867
## iter 80 value 30.267384
## iter 90 value 29.897241
## iter 100 value 29.748739
## final value 29.748739
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1151.951855
## iter 10 value 124.567826
## iter 20 value 75.044227
## iter 30 value 42.014264
## iter 40 value 29.563490
## iter 50 value 25.734536
## iter 60 value 22.800012
## iter 70 value 21.917350
## iter 80 value 21.157268
## iter 90 value 20.666322
## iter 100 value 20.290495
## final value 20.290495
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1358.137205
## iter 10 value 179.201581
## iter 20 value 113.289608
## iter 30 value 84.384391
## iter 40 value 66.244307
## iter 50 value 51.468837
## iter 60 value 46.362039
## iter 70 value 43.971264
## iter 80 value 42.361336
## iter 90 value 41.458540
## iter 100 value 40.397606
## final value 40.397606
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 733.657555
## iter 10 value 102.678178
## iter 20 value 59.977289
## iter 30 value 34.034233
## iter 40 value 22.103171
## iter 50 value 14.458051
## iter 60 value 12.371135
## iter 70 value 10.710288
## iter 80 value 9.065336
## iter 90 value 7.165871
## iter 100 value 5.846204
## final value 5.846204
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 774.499031
## iter 10 value 110.923521
## iter 20 value 69.446820
## iter 30 value 37.742493
## iter 40 value 23.002902
## iter 50 value 19.829243
## iter 60 value 18.883275
## iter 70 value 18.015924
## iter 80 value 17.431816
## iter 90 value 16.753207
## iter 100 value 16.195062
## final value 16.195062
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 759.046402
## iter 10 value 112.728319
## iter 20 value 70.166622
## iter 30 value 42.292826
## iter 40 value 25.203338
## iter 50 value 13.829150
## iter 60 value 8.007213
## iter 70 value 4.663812
## iter 80 value 3.966216
## iter 90 value 3.631321
## iter 100 value 3.263137
## final value 3.263137
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 724.271411
## iter 10 value 107.883803
## iter 20 value 54.581395
## iter 30 value 31.618682
## iter 40 value 16.069382
## iter 50 value 12.096206
## iter 60 value 11.384825
## iter 70 value 10.491024
## iter 80 value 9.612208
## iter 90 value 9.171526
## iter 100 value 8.628371
## final value 8.628371
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 837.329912
## iter 10 value 127.562002
## iter 20 value 79.525272
## iter 30 value 54.766585
## iter 40 value 30.088894
## iter 50 value 23.079181
## iter 60 value 21.263158
## iter 70 value 19.587461
## iter 80 value 18.443387
## iter 90 value 16.104577
## iter 100 value 14.836626
## final value 14.836626
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1115.511549
## iter 10 value 122.273436
## iter 20 value 65.387697
## iter 30 value 51.116442
## iter 40 value 42.648455
## iter 50 value 38.741595
## iter 60 value 34.221749
## iter 70 value 31.098805
## iter 80 value 26.712507
## iter 90 value 24.051676
## iter 100 value 23.085761
## final value 23.085761
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1027.784742
## iter 10 value 93.774779
## iter 20 value 49.619631
## iter 30 value 27.161296
## iter 40 value 13.576336
## iter 50 value 11.280569
## iter 60 value 10.784814
## iter 70 value 10.488353
## iter 80 value 10.300602
## iter 90 value 10.142218
## iter 100 value 9.925902
## final value 9.925902
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 844.858092
## iter 10 value 123.949406
## iter 20 value 71.699700
## iter 30 value 49.980986
## iter 40 value 42.261351
## iter 50 value 39.420324
## iter 60 value 37.538308
## iter 70 value 35.739813
## iter 80 value 33.818801
## iter 90 value 32.762115
## iter 100 value 32.272834
## final value 32.272834
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 800.456239
## iter 10 value 88.634855
## iter 20 value 53.650159
## iter 30 value 33.133687
## iter 40 value 21.945304
## iter 50 value 19.165351
## iter 60 value 17.215053
## iter 70 value 15.569437
## iter 80 value 14.968970
## iter 90 value 14.515175
## iter 100 value 14.180222
## final value 14.180222
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 840.563647
## iter 10 value 134.176163
## iter 20 value 101.023649
## iter 30 value 80.675980
## iter 40 value 67.370306
## iter 50 value 53.961224
## iter 60 value 44.365192
## iter 70 value 41.494654
## iter 80 value 40.533841
## iter 90 value 39.533751
## iter 100 value 38.632568
## final value 38.632568
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1210.234708
## iter 10 value 132.389361
## iter 20 value 79.635223
## iter 30 value 67.579159
## iter 40 value 55.628514
## iter 50 value 50.405837
## iter 60 value 37.217855
## iter 70 value 27.258107
## iter 80 value 23.859093
## iter 90 value 22.671857
## iter 100 value 22.028143
## final value 22.028143
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 852.478637
## iter 10 value 185.957833
## iter 20 value 98.716417
## iter 30 value 72.627192
## iter 40 value 62.389435
## iter 50 value 55.587293
## iter 60 value 50.988719
## iter 70 value 46.550925
## iter 80 value 44.434012
## iter 90 value 43.580725
## iter 100 value 42.940530
## final value 42.940530
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 816.908351
## iter 10 value 134.484827
## iter 20 value 102.807615
## iter 30 value 73.575576
## iter 40 value 47.322689
## iter 50 value 31.526156
## iter 60 value 27.718861
## iter 70 value 25.563092
## iter 80 value 24.047005
## iter 90 value 22.844585
## iter 100 value 22.123140
## final value 22.123140
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 741.618044
## iter 10 value 123.177986
## iter 20 value 79.863538
## iter 30 value 50.230049
## iter 40 value 29.107079
## iter 50 value 23.858140
## iter 60 value 17.528799
## iter 70 value 14.619176
## iter 80 value 13.551251
## iter 90 value 13.314444
## iter 100 value 13.154874
## final value 13.154874
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 846.690747
## iter 10 value 105.127587
## iter 20 value 57.448872
## iter 30 value 46.220912
## iter 40 value 36.982996
## iter 50 value 31.763133
## iter 60 value 27.667717
## iter 70 value 24.640130
## iter 80 value 23.251822
## iter 90 value 21.299082
## iter 100 value 18.695348
## final value 18.695348
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 731.739940
## iter 10 value 107.465636
## iter 20 value 69.512507
## iter 30 value 44.720141
## iter 40 value 24.550293
## iter 50 value 14.433048
## iter 60 value 10.586610
## iter 70 value 9.429045
## iter 80 value 8.678849
## iter 90 value 8.242896
## iter 100 value 7.966214
## final value 7.966214
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 776.095111
## iter 10 value 122.483876
## iter 20 value 84.383021
## iter 30 value 61.367553
## iter 40 value 46.770993
## iter 50 value 36.832989
## iter 60 value 33.613768
## iter 70 value 31.073527
## iter 80 value 29.720662
## iter 90 value 29.092456
## iter 100 value 28.350786
## final value 28.350786
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1251.722744
## iter 10 value 137.872808
## iter 20 value 91.660808
## iter 30 value 68.085672
## iter 40 value 58.439424
## iter 50 value 49.750983
## iter 60 value 46.440052
## iter 70 value 41.608244
## iter 80 value 40.817237
## iter 90 value 40.505015
## iter 100 value 40.152440
## final value 40.152440
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1278.385335
## iter 10 value 121.027259
## iter 20 value 67.655183
## iter 30 value 46.643204
## iter 40 value 36.746032
## iter 50 value 29.401185
## iter 60 value 25.622634
## iter 70 value 24.933154
## iter 80 value 23.813857
## iter 90 value 23.278364
## iter 100 value 23.003995
## final value 23.003995
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 692.288556
## iter 10 value 128.256415
## iter 20 value 82.085539
## iter 30 value 62.484035
## iter 40 value 55.339238
## iter 50 value 52.126104
## iter 60 value 50.363725
## iter 70 value 48.796693
## iter 80 value 46.063190
## iter 90 value 43.520389
## iter 100 value 42.279960
## final value 42.279960
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 652.775751
## iter 10 value 99.378971
## iter 20 value 57.183530
## iter 30 value 42.192476
## iter 40 value 36.272756
## iter 50 value 34.588252
## iter 60 value 33.650457
## iter 70 value 33.104900
## iter 80 value 32.660781
## iter 90 value 32.336350
## iter 100 value 32.092796
## final value 32.092796
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 805.955605
## iter 10 value 100.430863
## iter 20 value 65.262785
## iter 30 value 48.992571
## iter 40 value 30.838815
## iter 50 value 19.347329
## iter 60 value 16.363415
## iter 70 value 15.337531
## iter 80 value 13.718377
## iter 90 value 12.398038
## iter 100 value 10.290998
## final value 10.290998
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 740.455161
## iter 10 value 84.733860
## iter 20 value 62.468608
## iter 30 value 43.247724
## iter 40 value 37.029289
## iter 50 value 32.483983
## iter 60 value 26.862600
## iter 70 value 24.758712
## iter 80 value 22.903469
## iter 90 value 22.421556
## iter 100 value 21.826807
## final value 21.826807
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1484.105353
## iter 10 value 123.652850
## iter 20 value 72.295267
## iter 30 value 41.721534
## iter 40 value 29.333032
## iter 50 value 25.844663
## iter 60 value 24.168034
## iter 70 value 23.255630
## iter 80 value 22.731249
## iter 90 value 22.177385
## iter 100 value 21.048800
## final value 21.048800
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 685.549013
## iter 10 value 94.143507
## iter 20 value 58.802001
## iter 30 value 38.184590
## iter 40 value 26.328967
## iter 50 value 23.781130
## iter 60 value 21.295296
## iter 70 value 19.873051
## iter 80 value 19.022046
## iter 90 value 18.289589
## iter 100 value 16.498155
## final value 16.498155
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 686.006745
## iter 10 value 107.398397
## iter 20 value 52.842118
## iter 30 value 27.752727
## iter 40 value 20.015413
## iter 50 value 18.465519
## iter 60 value 17.823546
## iter 70 value 17.251858
## iter 80 value 16.982281
## iter 90 value 16.734737
## iter 100 value 16.561098
## final value 16.561098
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1635.684481
## iter 10 value 70.991439
## iter 20 value 33.163194
## iter 30 value 20.509614
## iter 40 value 13.063088
## iter 50 value 11.847084
## iter 60 value 11.368307
## iter 70 value 11.047674
## iter 80 value 9.095335
## iter 90 value 7.738106
## iter 100 value 6.842572
## final value 6.842572
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 970.242041
## iter 10 value 128.056365
## iter 20 value 83.205350
## iter 30 value 61.551260
## iter 40 value 44.804128
## iter 50 value 25.351303
## iter 60 value 16.900160
## iter 70 value 14.045674
## iter 80 value 12.493205
## iter 90 value 11.934168
## iter 100 value 11.603843
## final value 11.603843
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 934.431400
## iter 10 value 110.148626
## iter 20 value 73.312360
## iter 30 value 43.622068
## iter 40 value 28.802870
## iter 50 value 23.472215
## iter 60 value 21.856272
## iter 70 value 20.988328
## iter 80 value 20.554281
## iter 90 value 20.072491
## iter 100 value 19.799407
## final value 19.799407
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1916.831215
## iter 10 value 113.986688
## iter 20 value 91.571313
## iter 30 value 64.093314
## iter 40 value 51.357448
## iter 50 value 43.970001
## iter 60 value 40.581137
## iter 70 value 36.569706
## iter 80 value 31.910812
## iter 90 value 28.095541
## iter 100 value 26.058945
## final value 26.058945
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 689.564445
## iter 10 value 137.095301
## iter 20 value 67.240074
## iter 30 value 44.832767
## iter 40 value 33.440265
## iter 50 value 20.441586
## iter 60 value 16.839369
## iter 70 value 15.155907
## iter 80 value 12.515161
## iter 90 value 10.369242
## iter 100 value 9.310668
## final value 9.310668
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 705.875513
## iter 10 value 127.419625
## iter 20 value 74.359367
## iter 30 value 38.914190
## iter 40 value 22.835872
## iter 50 value 18.806960
## iter 60 value 17.461729
## iter 70 value 16.795280
## iter 80 value 16.261857
## iter 90 value 15.840665
## iter 100 value 15.521220
## final value 15.521220
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 939.300225
## iter 10 value 98.801687
## iter 20 value 65.270161
## iter 30 value 37.159234
## iter 40 value 22.188589
## iter 50 value 19.375101
## iter 60 value 18.519800
## iter 70 value 18.090096
## iter 80 value 17.685573
## iter 90 value 17.423244
## iter 100 value 17.173590
## final value 17.173590
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 817.828435
## iter 10 value 123.430171
## iter 20 value 80.547418
## iter 30 value 50.907271
## iter 40 value 39.807319
## iter 50 value 33.878975
## iter 60 value 31.328223
## iter 70 value 30.289481
## iter 80 value 29.545352
## iter 90 value 29.099476
## iter 100 value 28.879920
## final value 28.879920
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 975.026493
## iter 10 value 127.839084
## iter 20 value 76.609418
## iter 30 value 45.934857
## iter 40 value 32.444435
## iter 50 value 24.112157
## iter 60 value 17.813194
## iter 70 value 16.154892
## iter 80 value 15.039471
## iter 90 value 13.782909
## iter 100 value 13.015046
## final value 13.015046
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1002.274236
## iter 10 value 182.526404
## iter 20 value 118.677075
## iter 30 value 99.503281
## iter 40 value 82.854479
## iter 50 value 75.600683
## iter 60 value 66.316462
## iter 70 value 63.538243
## iter 80 value 61.580904
## iter 90 value 58.235522
## iter 100 value 56.053489
## final value 56.053489
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 884.953677
## iter 10 value 121.555119
## iter 20 value 55.689277
## iter 30 value 39.682138
## iter 40 value 31.779462
## iter 50 value 21.845434
## iter 60 value 19.657847
## iter 70 value 18.700435
## iter 80 value 17.956890
## iter 90 value 17.552435
## iter 100 value 16.257521
## final value 16.257521
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1242.911533
## iter 10 value 140.379707
## iter 20 value 89.869519
## iter 30 value 59.466649
## iter 40 value 35.823412
## iter 50 value 32.315454
## iter 60 value 28.881519
## iter 70 value 27.650469
## iter 80 value 24.454074
## iter 90 value 21.109346
## iter 100 value 20.033980
## final value 20.033980
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1117.670887
## iter 10 value 106.051677
## iter 20 value 49.985345
## iter 30 value 35.147247
## iter 40 value 26.280273
## iter 50 value 20.974160
## iter 60 value 18.958363
## iter 70 value 17.833195
## iter 80 value 15.461392
## iter 90 value 12.338972
## iter 100 value 11.214234
## final value 11.214234
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 977.580939
## iter 10 value 98.807783
## iter 20 value 66.515751
## iter 30 value 41.060825
## iter 40 value 26.888098
## iter 50 value 22.647100
## iter 60 value 18.292072
## iter 70 value 15.917123
## iter 80 value 14.730335
## iter 90 value 13.636750
## iter 100 value 11.164016
## final value 11.164016
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 721.915109
## iter 10 value 102.614474
## iter 20 value 72.822135
## iter 30 value 46.123930
## iter 40 value 36.545162
## iter 50 value 33.945586
## iter 60 value 32.745472
## iter 70 value 31.705419
## iter 80 value 28.474133
## iter 90 value 26.534402
## iter 100 value 22.011529
## final value 22.011529
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1456.117191
## iter 10 value 119.196743
## iter 20 value 78.101259
## iter 30 value 51.776931
## iter 40 value 32.428918
## iter 50 value 25.771807
## iter 60 value 23.478371
## iter 70 value 21.484476
## iter 80 value 20.761899
## iter 90 value 19.886399
## iter 100 value 19.447605
## final value 19.447605
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1145.131937
## iter 10 value 128.852071
## iter 20 value 92.161026
## iter 30 value 64.095128
## iter 40 value 54.075980
## iter 50 value 50.339618
## iter 60 value 44.341365
## iter 70 value 40.983495
## iter 80 value 37.801596
## iter 90 value 36.796359
## iter 100 value 35.708110
## final value 35.708110
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 710.925998
## iter 10 value 104.972487
## iter 20 value 58.820804
## iter 30 value 43.140447
## iter 40 value 40.354871
## iter 50 value 38.415615
## iter 60 value 37.172685
## iter 70 value 35.235502
## iter 80 value 31.862181
## iter 90 value 27.081978
## iter 100 value 23.678942
## final value 23.678942
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 688.366574
## iter 10 value 111.555443
## iter 20 value 76.299656
## iter 30 value 49.458356
## iter 40 value 33.746939
## iter 50 value 29.943777
## iter 60 value 25.952913
## iter 70 value 24.193265
## iter 80 value 22.958479
## iter 90 value 21.116093
## iter 100 value 20.675943
## final value 20.675943
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1200.183847
## iter 10 value 95.313223
## iter 20 value 54.681344
## iter 30 value 38.800580
## iter 40 value 28.736961
## iter 50 value 24.303761
## iter 60 value 21.933719
## iter 70 value 20.429373
## iter 80 value 19.588020
## iter 90 value 18.782642
## iter 100 value 17.720185
## final value 17.720185
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1377.551614
## iter 10 value 116.784299
## iter 20 value 80.401813
## iter 30 value 59.114034
## iter 40 value 43.933751
## iter 50 value 38.820006
## iter 60 value 33.799017
## iter 70 value 31.777224
## iter 80 value 30.902362
## iter 90 value 29.639048
## iter 100 value 28.472579
## final value 28.472579
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1170.499960
## iter 10 value 208.665083
## iter 20 value 161.408087
## iter 30 value 121.939530
## iter 40 value 108.079109
## iter 50 value 93.472908
## iter 60 value 78.598132
## iter 70 value 72.557188
## iter 80 value 68.877665
## iter 90 value 67.433786
## iter 100 value 64.655355
## final value 64.655355
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 610.749440
## iter 10 value 84.571058
## iter 20 value 45.658029
## iter 30 value 13.464824
## iter 40 value 8.229495
## iter 50 value 7.375350
## iter 60 value 6.592863
## iter 70 value 5.327142
## iter 80 value 4.401347
## iter 90 value 3.928416
## iter 100 value 3.613900
## final value 3.613900
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 567.811156
## iter 10 value 88.356850
## iter 20 value 57.436093
## iter 30 value 48.032355
## iter 40 value 37.311579
## iter 50 value 28.558229
## iter 60 value 24.641139
## iter 70 value 21.652453
## iter 80 value 17.332603
## iter 90 value 16.273340
## iter 100 value 15.307912
## final value 15.307912
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 862.536820
## iter 10 value 115.623766
## iter 20 value 57.350797
## iter 30 value 39.705021
## iter 40 value 32.893522
## iter 50 value 29.891843
## iter 60 value 28.127074
## iter 70 value 25.497308
## iter 80 value 21.826556
## iter 90 value 18.896095
## iter 100 value 16.585063
## final value 16.585063
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 842.789151
## iter 10 value 112.303464
## iter 20 value 54.972600
## iter 30 value 25.604243
## iter 40 value 9.588979
## iter 50 value 6.147195
## iter 60 value 5.372688
## iter 70 value 3.712201
## iter 80 value 3.284634
## iter 90 value 3.029432
## iter 100 value 2.854277
## final value 2.854277
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 824.952208
## iter 10 value 93.942633
## iter 20 value 50.027608
## iter 30 value 15.591496
## iter 40 value 7.276256
## iter 50 value 5.435698
## iter 60 value 4.706206
## iter 70 value 4.305903
## iter 80 value 4.013879
## iter 90 value 3.776400
## iter 100 value 3.606745
## final value 3.606745
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1940.586577
## iter 10 value 112.739030
## iter 20 value 64.444047
## iter 30 value 38.810530
## iter 40 value 26.553578
## iter 50 value 15.115238
## iter 60 value 11.336617
## iter 70 value 9.999602
## iter 80 value 9.100527
## iter 90 value 7.853471
## iter 100 value 7.530020
## final value 7.530020
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 774.208030
## iter 10 value 144.834209
## iter 20 value 91.187098
## iter 30 value 79.422368
## iter 40 value 73.688551
## iter 50 value 70.018323
## iter 60 value 66.641100
## iter 70 value 59.562532
## iter 80 value 53.278285
## iter 90 value 49.598729
## iter 100 value 45.722099
## final value 45.722099
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 805.726964
## iter 10 value 133.886849
## iter 20 value 82.160916
## iter 30 value 61.363131
## iter 40 value 54.321395
## iter 50 value 49.372203
## iter 60 value 45.174245
## iter 70 value 41.130452
## iter 80 value 40.023664
## iter 90 value 38.213378
## iter 100 value 37.049480
## final value 37.049480
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 719.905968
## iter 10 value 127.547069
## iter 20 value 92.850694
## iter 30 value 78.949523
## iter 40 value 72.934603
## iter 50 value 67.946854
## iter 60 value 61.908934
## iter 70 value 59.303117
## iter 80 value 56.301451
## iter 90 value 53.525314
## iter 100 value 51.793049
## final value 51.793049
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1123.474406
## iter 10 value 108.741811
## iter 20 value 60.993614
## iter 30 value 51.043630
## iter 40 value 47.679781
## iter 50 value 46.719674
## iter 60 value 42.263116
## iter 70 value 41.150027
## iter 80 value 40.881030
## iter 90 value 40.754346
## iter 100 value 40.710754
## final value 40.710754
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 887.976516
## iter 10 value 140.218582
## iter 20 value 81.057850
## iter 30 value 60.016911
## iter 40 value 51.479782
## iter 50 value 46.113686
## iter 60 value 44.554834
## iter 70 value 40.352567
## iter 80 value 39.168057
## iter 90 value 37.967781
## iter 100 value 36.746812
## final value 36.746812
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1153.814173
## iter 10 value 158.623607
## iter 20 value 104.933210
## iter 30 value 87.987257
## iter 40 value 79.094384
## iter 50 value 72.144498
## iter 60 value 68.229333
## iter 70 value 66.371337
## iter 80 value 65.294755
## iter 90 value 64.253520
## iter 100 value 63.731293
## final value 63.731293
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 932.279385
## iter 10 value 85.666244
## iter 20 value 37.881832
## iter 30 value 14.823605
## iter 40 value 3.859718
## iter 50 value 2.174565
## iter 60 value 1.968506
## iter 70 value 1.839730
## iter 80 value 1.786296
## iter 90 value 1.755230
## iter 100 value 1.726047
## final value 1.726047
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 996.832802
## iter 10 value 79.503552
## iter 20 value 47.233345
## iter 30 value 21.059769
## iter 40 value 12.741676
## iter 50 value 11.574312
## iter 60 value 10.700190
## iter 70 value 10.223247
## iter 80 value 9.454272
## iter 90 value 9.240277
## iter 100 value 8.926146
## final value 8.926146
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 1357.402644
## iter 10 value 69.701405
## iter 20 value 33.335903
## iter 30 value 15.339783
## iter 40 value 5.502410
## iter 50 value 2.899423
## iter 60 value 2.381366
## iter 70 value 2.044604
## iter 80 value 1.908371
## iter 90 value 1.796813
## iter 100 value 1.749417
## final value 1.749417
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1163.695115
## iter 10 value 107.097729
## iter 20 value 59.697209
## iter 30 value 31.614859
## iter 40 value 18.021113
## iter 50 value 11.732213
## iter 60 value 6.777037
## iter 70 value 4.161085
## iter 80 value 3.017966
## iter 90 value 2.570484
## iter 100 value 2.361752
## final value 2.361752
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1001.535628
## iter 10 value 129.507931
## iter 20 value 68.581157
## iter 30 value 34.810910
## iter 40 value 19.094587
## iter 50 value 14.875110
## iter 60 value 12.804052
## iter 70 value 11.707010
## iter 80 value 10.894926
## iter 90 value 7.143437
## iter 100 value 4.842953
## final value 4.842953
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1489.905583
## iter 10 value 104.094551
## iter 20 value 68.739603
## iter 30 value 49.469462
## iter 40 value 34.804062
## iter 50 value 24.814726
## iter 60 value 15.823334
## iter 70 value 12.325983
## iter 80 value 10.038910
## iter 90 value 8.892734
## iter 100 value 8.320424
## final value 8.320424
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 898.951682
## iter 10 value 114.298535
## iter 20 value 68.363576
## iter 30 value 39.197717
## iter 40 value 21.050613
## iter 50 value 15.589951
## iter 60 value 13.664363
## iter 70 value 12.311030
## iter 80 value 10.750577
## iter 90 value 10.278116
## iter 100 value 9.987362
## final value 9.987362
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 672.707348
## iter 10 value 106.359297
## iter 20 value 67.800989
## iter 30 value 40.746975
## iter 40 value 27.301432
## iter 50 value 22.311648
## iter 60 value 20.863007
## iter 70 value 19.361392
## iter 80 value 16.384897
## iter 90 value 14.227147
## iter 100 value 12.314283
## final value 12.314283
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 683.756283
## iter 10 value 141.835856
## iter 20 value 92.368599
## iter 30 value 74.311289
## iter 40 value 61.817844
## iter 50 value 56.721229
## iter 60 value 52.648806
## iter 70 value 50.648804
## iter 80 value 49.049133
## iter 90 value 48.038947
## iter 100 value 47.267209
## final value 47.267209
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 875.637025
## iter 10 value 125.354224
## iter 20 value 79.257867
## iter 30 value 57.461144
## iter 40 value 35.343814
## iter 50 value 20.992355
## iter 60 value 17.888774
## iter 70 value 15.706097
## iter 80 value 14.864244
## iter 90 value 14.515880
## iter 100 value 14.274596
## final value 14.274596
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 911.695409
## iter 10 value 173.736032
## iter 20 value 78.864149
## iter 30 value 54.965343
## iter 40 value 36.207268
## iter 50 value 22.123184
## iter 60 value 17.260058
## iter 70 value 14.505485
## iter 80 value 13.158879
## iter 90 value 12.161114
## iter 100 value 11.051777
## final value 11.051777
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1267.902004
## iter 10 value 141.042504
## iter 20 value 99.068495
## iter 30 value 81.562901
## iter 40 value 64.635065
## iter 50 value 54.476064
## iter 60 value 50.013682
## iter 70 value 48.385669
## iter 80 value 47.138472
## iter 90 value 45.101957
## iter 100 value 43.840018
## final value 43.840018
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1154.100191
## iter 10 value 112.016569
## iter 20 value 67.165853
## iter 30 value 42.055617
## iter 40 value 27.790011
## iter 50 value 24.628146
## iter 60 value 23.361786
## iter 70 value 22.506127
## iter 80 value 21.393918
## iter 90 value 20.806996
## iter 100 value 19.889128
## final value 19.889128
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1146.858425
## iter 10 value 95.505485
## iter 20 value 47.565928
## iter 30 value 33.970358
## iter 40 value 28.211063
## iter 50 value 25.085763
## iter 60 value 23.455459
## iter 70 value 21.988628
## iter 80 value 20.667916
## iter 90 value 19.022554
## iter 100 value 18.523736
## final value 18.523736
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 980.663294
## iter 10 value 118.314535
## iter 20 value 80.111219
## iter 30 value 65.222298
## iter 40 value 56.596098
## iter 50 value 53.938328
## iter 60 value 49.803268
## iter 70 value 47.953653
## iter 80 value 46.908759
## iter 90 value 46.225982
## iter 100 value 45.757848
## final value 45.757848
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 729.770454
## iter 10 value 103.698782
## iter 20 value 66.113972
## iter 30 value 31.825145
## iter 40 value 20.893140
## iter 50 value 17.902533
## iter 60 value 14.596533
## iter 70 value 13.414593
## iter 80 value 11.695185
## iter 90 value 10.162209
## iter 100 value 8.231707
## final value 8.231707
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 687.276380
## iter 10 value 129.284599
## iter 20 value 85.679688
## iter 30 value 64.756419
## iter 40 value 56.527490
## iter 50 value 52.390330
## iter 60 value 50.618219
## iter 70 value 49.495012
## iter 80 value 47.365495
## iter 90 value 46.161605
## iter 100 value 45.589344
## final value 45.589344
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 962.737771
## iter 10 value 160.443973
## iter 20 value 107.102251
## iter 30 value 79.270607
## iter 40 value 62.145554
## iter 50 value 50.862700
## iter 60 value 44.401342
## iter 70 value 41.169957
## iter 80 value 40.080139
## iter 90 value 38.964238
## iter 100 value 38.395458
## final value 38.395458
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 747.445359
## iter 10 value 101.409583
## iter 20 value 55.132631
## iter 30 value 35.137165
## iter 40 value 24.049569
## iter 50 value 21.129293
## iter 60 value 20.087061
## iter 70 value 19.168364
## iter 80 value 18.388976
## iter 90 value 17.677899
## iter 100 value 16.501509
## final value 16.501509
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 729.394853
## iter 10 value 130.358294
## iter 20 value 88.870822
## iter 30 value 45.066609
## iter 40 value 30.686133
## iter 50 value 25.262995
## iter 60 value 23.627448
## iter 70 value 22.121564
## iter 80 value 21.262267
## iter 90 value 20.569613
## iter 100 value 19.690617
## final value 19.690617
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 769.887057
## iter 10 value 250.691725
## iter 20 value 115.786269
## iter 30 value 74.352608
## iter 40 value 58.869532
## iter 50 value 48.395353
## iter 60 value 43.615909
## iter 70 value 41.475403
## iter 80 value 38.502683
## iter 90 value 37.610913
## iter 100 value 35.371732
## final value 35.371732
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 739.545913
## iter 10 value 122.137131
## iter 20 value 62.818913
## iter 30 value 35.361655
## iter 40 value 20.714145
## iter 50 value 17.385435
## iter 60 value 15.661885
## iter 70 value 15.091212
## iter 80 value 14.352896
## iter 90 value 13.968978
## iter 100 value 13.754662
## final value 13.754662
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 832.266490
## iter 10 value 135.473053
## iter 20 value 85.167117
## iter 30 value 45.354567
## iter 40 value 30.204455
## iter 50 value 26.252872
## iter 60 value 24.674807
## iter 70 value 23.902299
## iter 80 value 20.783121
## iter 90 value 18.655749
## iter 100 value 16.825341
## final value 16.825341
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 945.721672
## iter 10 value 155.545797
## iter 20 value 108.923274
## iter 30 value 77.956013
## iter 40 value 54.603048
## iter 50 value 40.022530
## iter 60 value 32.993727
## iter 70 value 31.265393
## iter 80 value 30.203203
## iter 90 value 29.392724
## iter 100 value 28.414600
## final value 28.414600
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 1909.544089
## iter 10 value 89.952604
## iter 20 value 45.680418
## iter 30 value 20.026754
## iter 40 value 8.450538
## iter 50 value 6.711918
## iter 60 value 5.744847
## iter 70 value 5.270611
## iter 80 value 4.802945
## iter 90 value 4.522376
## iter 100 value 4.366331
## final value 4.366331
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1228.831718
## iter 10 value 101.364904
## iter 20 value 34.327315
## iter 30 value 15.488030
## iter 40 value 6.859993
## iter 50 value 5.102470
## iter 60 value 4.277216
## iter 70 value 3.900847
## iter 80 value 3.768320
## iter 90 value 3.698184
## iter 100 value 3.505140
## final value 3.505140
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 790.670866
## iter 10 value 89.704971
## iter 20 value 47.928014
## iter 30 value 27.847463
## iter 40 value 20.144327
## iter 50 value 18.510185
## iter 60 value 16.439410
## iter 70 value 14.057668
## iter 80 value 12.100558
## iter 90 value 9.328826
## iter 100 value 7.803805
## final value 7.803805
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 1094.445458
## iter 10 value 92.878399
## iter 20 value 55.126762
## iter 30 value 40.410829
## iter 40 value 33.497273
## iter 50 value 27.582483
## iter 60 value 24.585911
## iter 70 value 21.994941
## iter 80 value 20.680279
## iter 90 value 19.499192
## iter 100 value 14.890174
## final value 14.890174
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 1203.846570
## iter 10 value 77.356188
## iter 20 value 36.738083
## iter 30 value 29.010759
## iter 40 value 26.293315
## iter 50 value 23.811808
## iter 60 value 22.312338
## iter 70 value 18.218925
## iter 80 value 17.029016
## iter 90 value 16.351007
## iter 100 value 12.172274
## final value 12.172274
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 985.845976
## iter 10 value 114.546227
## iter 20 value 67.416439
## iter 30 value 43.782150
## iter 40 value 31.692796
## iter 50 value 25.904118
## iter 60 value 23.945662
## iter 70 value 22.441549
## iter 80 value 21.943997
## iter 90 value 20.440112
## iter 100 value 19.702869
## final value 19.702869
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001
## # weights: 169
## initial value 715.923104
## iter 10 value 119.793042
## iter 20 value 71.565992
## iter 30 value 54.800901
## iter 40 value 43.250546
## iter 50 value 37.946321
## iter 60 value 32.617408
## iter 70 value 29.341663
## iter 80 value 27.331432
## iter 90 value 25.936076
## iter 100 value 25.361411
## final value 25.361411
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001
## + Fold2: size=6, decay=0.001
## # weights: 169
## initial value 1143.133598
## iter 10 value 120.792246
## iter 20 value 70.063983
## iter 30 value 50.314463
## iter 40 value 37.543539
## iter 50 value 32.181351
## iter 60 value 29.215253
## iter 70 value 28.107129
## iter 80 value 27.689545
## iter 90 value 25.617763
## iter 100 value 24.581040
## final value 24.581040
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001
## + Fold3: size=6, decay=0.001
## # weights: 169
## initial value 711.153775
## iter 10 value 114.895382
## iter 20 value 70.767249
## iter 30 value 45.043846
## iter 40 value 37.753008
## iter 50 value 33.635730
## iter 60 value 31.629516
## iter 70 value 30.403743
## iter 80 value 29.419789
## iter 90 value 28.578127
## iter 100 value 27.882937
## final value 27.882937
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001
## + Fold4: size=6, decay=0.001
## # weights: 169
## initial value 828.628104
## iter 10 value 110.857985
## iter 20 value 51.189862
## iter 30 value 21.719964
## iter 40 value 12.469527
## iter 50 value 11.200246
## iter 60 value 10.541256
## iter 70 value 10.212894
## iter 80 value 10.027629
## iter 90 value 9.925835
## iter 100 value 9.855516
## final value 9.855516
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001
## + Fold5: size=6, decay=0.001
## # weights: 169
## initial value 936.848406
## iter 10 value 105.675444
## iter 20 value 66.416936
## iter 30 value 49.041741
## iter 40 value 41.117142
## iter 50 value 37.767796
## iter 60 value 34.731607
## iter 70 value 32.901691
## iter 80 value 30.806288
## iter 90 value 29.668409
## iter 100 value 28.154290
## final value 28.154290
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001
## Aggregating results
## Fitting final model on full training set
## # weights: 169
## initial value 1543.491617
## iter 10 value 185.342493
## iter 20 value 108.712491
## iter 30 value 84.929788
## iter 40 value 74.268272
## iter 50 value 66.445090
## iter 60 value 63.500129
## iter 70 value 61.887138
## iter 80 value 60.429791
## iter 90 value 58.587768
## iter 100 value 56.085874
## final value 56.085874
## stopped after 100 iterations
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "threshold value",
names = seq(0.05, 0.5, 0.05), col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] 0.2112964 0.2108795 0.2130263 0.2101084 0.2107545 0.2110046 0.2112130
## [8] 0.2122551 0.2092955 0.2099416
We make the final prediction with the optimal threshold.
index = which.max(medians)
threshold = seq(0.05, 0.5, 0.05)[index]
nnProb = predict(nn.train, newdata=test_data, type="prob")
nnPred = rep("NOT.QSO", nrow(test_data))
nnPred[which(nnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(nnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.2138356
We have a final economic profit of 0.2138356, which is good, but still not better than the decision tree model.
We compute the variable importance to see which one is the more relevant in this model.
nn_imp <- varImp(nn.train, scale = F)
plot(nn_imp, scales = list(y = list(cex = .95)))
redshift and u are the more relevant
variables.
partial(nn.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
partial(nn.train, pred.var = "u", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
We can see that the higher value in u the closer to 0 is
the probability of being classified as a quasar object. The conclusion
with redshift is the same as always.
Deep neural networks are a type of neural network architecture that consists of multiple hidden layers between the input and output layers. These networks are characterized by their depth, meaning they have more than one hidden layer, allowing them to learn complex and hierarchical representations of data. Deep learning, which relies on deep neural networks, has emerged as a powerful tool for solving a wide range of machine learning tasks, including image and speech recognition, natural language processing, and many others.
Deep neural networks have revolutionized many fields of artificial intelligence and have achieved remarkable success in tasks that were previously considered challenging, such as image recognition, speech understanding, and language translation. Their ability to automatically learn hierarchical representations from data has made them indispensable tools in modern machine learning and artificial intelligence research.
We use caret to compute it.
dnn.train <- train(class ~.,
method = "dnn",
data = train_data,
preProcess = c("center", "scale"),
numepochs = 20, # number of iterations on the whole training set
tuneGrid = expand.grid(layer1 = 1:4,
layer2 = 0:2,
layer3 = 0:2,
hidden_dropout = 0,
visible_dropout = 0),
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
plot(dnn.train)
best_hyperparameters = dnn.train$bestTune
We change the threshold manually.
threshold = 0.4
dnnProb = predict(dnn.train, newdata=test_data, type="prob")
dnnPred = rep("NOT.QSO", nrow(test_data))
dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dnnPred), test_data$class)$table
## Warning in confusionMatrix.default(factor(dnnPred), test_data$class): Levels
## are not in the same order for reference and data. Refactoring data to match.
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.01037673
We compute the most optimal one, because we want to improve our economic profit 0.0103767.
profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold
grid = best_hyperparameters
j <- 0
for (threshold in seq(0.25, 0.7, 0.05)){
j <- j + 1
#cat(j)
for(i in 1:15){
# partition data intro training (75%) and testing sets (25%)
d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
# select training sample
train <- train_data[d,]
test <- train_data[-d,]
dnn.train <- train(class ~.,
method = "dnn",
data = train,
preProcess = c("center", "scale"),
numepochs = 20, # number of iterations on the whole training set
tuneGrid = grid,
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
dnnProb = predict(dnn.train, test, type="prob")
dnnPred = rep("NOT.QSO", nrow(test))
dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dnnPred), test$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
profit.i[i,j] <- profit
}
}
# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
ylab = "Economic profit",
xlab = "Threshold value",
names = seq(0.25, 0.7, 0.05), col="royalblue2",las=2)
# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians
## [1] -0.03288870 -0.03288870 0.06140058 0.01031680 0.01031680 0.01031680
## [7] 0.01031680 0.01031680 0.01031680 0.01031680
Final prediction with the optimal threshold.
index = which.max(medians)
threshold = seq(0.25, 0.7, 0.05)[index]
dnnProb = predict(dnn.train, newdata=test_data, type="prob")
dnnPred = rep("NOT.QSO", nrow(test_data))
dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dnnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.1070762
Incredibly bad performance as a model.
We study which are the most influential variables.
dnn_imp <- varImp(dnn.train, scale = F)
plot(dnn_imp, scales = list(y = list(cex = .95)))
Once again, redshift is the most important one. This
time the increase in probability to be classified as quasar object
increases constantly with the increase of this variable, up to
0.3610.
partial(dnn.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)
After all this model computing, we can see the best performing one is decision trees (economic profit = 0.2267878), followed by random forest (economic profit = 0.2245791) and then, support vector machines (economic profit = 0.2234289).
However, we have two more ideas that we are going to implement to see if we can improve them more: down-sampling and ensemble models.
Downsampling is a term commonly used in the context of machine learning, especially in problems involving imbalanced classification. It involves reducing the size of the majority class sample to match that of the minority class sample. This is done by randomly selecting an appropriate number of samples from the majority class, so that its size matches that of the minority class.
When working with imbalanced datasets, where one class has significantly more samples than another, machine learning algorithms may be biased towards the majority class and struggle to recognize patterns in the minority class. This can result in models that do not generalize well and perform poorly in predicting the minority class.
By using downsampling, the aim is to balance the dataset, which can improve the model’s performance by enabling it to more effectively learn the characteristics of both classes. However, it’s important to note that downsampling also involves loss of information, as samples from the majority class are being removed. Therefore, striking an appropriate balance between reducing bias and retaining necessary information for the model to learn correctly is crucial.
We introduce in our control function the choice to downsample.
ctrl$sampling <- "up"
Now we are going to train our three best samples using this method and see if they improve.
svmFit <- train(class ~., method = "svmRadial",
data = train_data,
preProcess = c("center", "scale"),
tuneGrid = svmhyp,
metric = "EconomicProfit", # maximizing the profit again
trControl = ctrl)
print(svmFit)
## Support Vector Machines with Radial Basis Function Kernel
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3199, 3200, 3200, 3200, 3201
## Addtional sampling using down-sampling prior to pre-processing
##
## Resampling results:
##
## EconomicProfit
## 0.2173361
##
## Tuning parameter 'sigma' was held constant at a value of 0.02
## Tuning
## parameter 'C' was held constant at a value of 0.75
Now we make the predictions and see if the economic profit improves.
svmProb = predict(svmFit, test_data, type="prob")
threshold = svmoptimal
svm.pred = rep("NOT.QSO", nrow(test_data)) # All good's
svm.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad
CM = confusionMatrix(factor(svm.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2130855
Our profit before down-sampling was 0.2234289 and now our profit is 0.2130855, so it decreased. Let’s try to see if it improves the decision trees.
fit.c50 <- train(class ~.,
data=train_data,
method="C5.0",
metric="EconomicProfit",
tuneGrid = dthyp,
trControl = ctrl)
fit.c50
## C5.0
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3201, 3201, 3199, 3199, 3200
## Addtional sampling using down-sampling
##
## Resampling results:
##
## EconomicProfit
## 0.2246358
##
## Tuning parameter 'trials' was held constant at a value of 10
## Tuning
## parameter 'model' was held constant at a value of tree
## Tuning
## parameter 'winnow' was held constant at a value of TRUE
threshold = dtoptimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit
## [1] 0.2267878
Our profit before down-sampling was 0.2267878 and now it is 0.2267878, which is the same economic impact. Lets’ take a look at our last of these 3 models.
rf.train <- train(class ~.,
method = "rf",
data = train_data,
preProcess = c("center", "scale"),
ntree = 200,
cutoff=c(0.7,0.3),
tuneGrid = rfhyp,
metric = "EconomicProfit",
maximize = F,
trControl = ctrl)
rf.train
## Random Forest
##
## 4000 samples
## 15 predictor
## 2 classes: 'NOT.QSO', 'QSO'
##
## Pre-processing: centered (26), scaled (26)
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 3201, 3200, 3199, 3200, 3200
## Addtional sampling using up-sampling prior to pre-processing
##
## Resampling results:
##
## EconomicProfit
## 0.2293999
##
## Tuning parameter 'mtry' was held constant at a value of 8
Once trained, we obtain our predictions and the economic profit.
threshold = rfoptimal
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
best_profit = sum(as.vector(CM)*profit.unit)/sum(CM)
best_profit
## [1] 0.2292215
The economic profit before down-sampling was 0.2245791 and now it is 0.2292215, it increased
Hence, out best model yet is random forest with down-sampling. We use a final approach to try and improve it (ensemble models).
Ensemble learning is a machine learning technique where multiple models are trained to solve the same problem, and their predictions are combined to make a final prediction. The idea behind ensemble learning is that by combining the predictions of multiple models, the overall performance can often be better than that of any individual model.
Ensemble learning can lead to improved generalization performance, robustness to overfitting, and better handling of complex datasets. It is widely used in various machine learning tasks, including classification, regression, and anomaly detection, and has been applied successfully in many real-world applications.
We are going our best 3 models: svm, decision trees and random forest before down-sampling. It is very computationally costly because we take economic profit into account.
First, we create an ensemble for classification with mode function.
mode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
Now, we make the predictions.
ensemble.pred = apply(data.frame(svm.pred, Cred.pred, rfPred), 1, mode)
CM = confusionMatrix(factor(ensemble.pred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit
## [1] 0.227863
Our best profit yet was that from random forest after down-sampling 0.2292215 and now we have 0.227863, so it did not improve it.
After all this implementation of numerous machine learning tools and the implementation of various techniques to improve the models, we have arrived at our best model. Our best model is random forest with down-sampling and the corresponding economic profit of the predictions is 0.2292215.